1
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Takase B, Ikeda T, Shimizu W, Abe H, Aiba T, Chinushi M, Koba S, Kusano K, Niwano S, Takahashi N, Takatsuki S, Tanno K, Watanabe E, Yoshioka K, Amino M, Fujino T, Iwasaki YK, Kohno R, Kinoshita T, Kurita Y, Masaki N, Murata H, Shinohara T, Yada H, Yodogawa K, Kimura T, Kurita T, Nogami A, Sumitomo N. JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia. Circ J 2024; 88:1509-1595. [PMID: 37690816 DOI: 10.1253/circj.cj-22-0827] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Affiliation(s)
| | - Takanori Ikeda
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine
| | - Wataru Shimizu
- Department of Cardiovascular Medicine, Nippon Medical School
| | - Haruhiko Abe
- Department of Heart Rhythm Management, University of Occupational and Environmental Health, Japan
| | - Takeshi Aiba
- Department of Clinical Laboratory Medicine and Genetics, National Cerebral and Cardiovascular Center
| | - Masaomi Chinushi
- School of Health Sciences, Niigata University School of Medicine
| | - Shinji Koba
- Division of Cardiology, Department of Medicine, Showa University School of Medicine
| | - Kengo Kusano
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center
| | - Shinichi Niwano
- Department of Cardiovascular Medicine, Kitasato University School of Medicine
| | - Naohiko Takahashi
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Seiji Takatsuki
- Department of Cardiology, Keio University School of Medicine
| | - Kaoru Tanno
- Cardiology Division, Cardiovascular Center, Showa University Koto-Toyosu Hospital
| | - Eiichi Watanabe
- Division of Cardiology, Department of Internal Medicine, Fujita Health University Bantane Hospital
| | | | - Mari Amino
- Department of Cardiology, Tokai University School of Medicine
| | - Tadashi Fujino
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine
| | - Yu-Ki Iwasaki
- Department of Cardiovascular Medicine, Nippon Medical School
| | - Ritsuko Kohno
- Department of Heart Rhythm Management, University of Occupational and Environmental Health, Japan
| | - Toshio Kinoshita
- Department of Cardiovascular Medicine, Toho University Faculty of Medicine
| | - Yasuo Kurita
- Cardiovascular Center, International University of Health and Welfare, Mita Hospital
| | - Nobuyuki Masaki
- Department of Intensive Care Medicine, National Defense Medical College
| | | | - Tetsuji Shinohara
- Department of Cardiology and Clinical Examination, Faculty of Medicine, Oita University
| | - Hirotaka Yada
- Department of Cardiology, International University of Health and Welfare, Mita Hospital
| | - Kenji Yodogawa
- Department of Cardiovascular Medicine, Nippon Medical School
| | - Takeshi Kimura
- Cardiovascular Medicine, Kyoto University Graduate School of Medicine
| | | | - Akihiko Nogami
- Department of Cardiology, Faculty of Medicine, University of Tsukuba
| | - Naokata Sumitomo
- Department of Pediatric Cardiology, Saitama Medical University International Medical Center
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2
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Sawano S, Kodera S, Setoguchi N, Tanabe K, Kushida S, Kanda J, Saji M, Nanasato M, Maki H, Fujita H, Kato N, Watanabe H, Suzuki M, Takahashi M, Sawada N, Yamasaki M, Sato M, Katsushika S, Shinohara H, Takeda N, Fujiu K, Daimon M, Akazawa H, Morita H, Komuro I. Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies. PLoS One 2024; 19:e0307978. [PMID: 39141600 PMCID: PMC11324121 DOI: 10.1371/journal.pone.0307978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 07/15/2024] [Indexed: 08/16/2024] Open
Abstract
The generalization of deep neural network algorithms to a broader population is an important challenge in the medical field. We aimed to apply self-supervised learning using masked autoencoders (MAEs) to improve the performance of the 12-lead electrocardiography (ECG) analysis model using limited ECG data. We pretrained Vision Transformer (ViT) models by reconstructing the masked ECG data with MAE. We fine-tuned this MAE-based ECG pretrained model on ECG-echocardiography data from The University of Tokyo Hospital (UTokyo) for the detection of left ventricular systolic dysfunction (LVSD), and then evaluated it using multi-center external validation data from seven institutions, employing the area under the receiver operating characteristic curve (AUROC) for assessment. We included 38,245 ECG-echocardiography pairs from UTokyo and 229,439 pairs from all institutions. The performances of MAE-based ECG models pretrained using ECG data from UTokyo were significantly higher than that of other Deep Neural Network models across all external validation cohorts (AUROC, 0.913-0.962 for LVSD, p < 0.001). Moreover, we also found improvements for the MAE-based ECG analysis model depending on the model capacity and the amount of training data. Additionally, the MAE-based ECG analysis model maintained high performance even on the ECG benchmark dataset (PTB-XL). Our proposed method developed high performance MAE-based ECG analysis models using limited ECG data.
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Affiliation(s)
- Shinnosuke Sawano
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Satoshi Kodera
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Naoto Setoguchi
- Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan
| | - Kengo Tanabe
- Division of Cardiology, Mitsui Memorial Hospital, Tokyo, Japan
| | - Shunichi Kushida
- Department of Cardiovascular Medicine, Asahi General Hospital, Chiba, Japan
| | - Junji Kanda
- Department of Cardiovascular Medicine, Asahi General Hospital, Chiba, Japan
| | - Mike Saji
- Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | - Mamoru Nanasato
- Department of Cardiology, Sakakibara Heart Institute, Tokyo, Japan
| | - Hisataka Maki
- Division of Cardiovascular Medicine, Saitama Medical Center, Jichi Medical University, Omiya, Japan
| | - Hideo Fujita
- Division of Cardiovascular Medicine, Saitama Medical Center, Jichi Medical University, Omiya, Japan
| | - Nahoko Kato
- Department of Cardiology, Tokyo Bay Medical Center, Urayasu, Japan
| | | | - Minami Suzuki
- Department of Cardiology, JR General Hospital, Tokyo, Japan
| | | | - Naoko Sawada
- Department of Cardiology, NTT Medical Center Tokyo, Tokyo, Japan
| | - Masao Yamasaki
- Department of Cardiology, NTT Medical Center Tokyo, Tokyo, Japan
| | - Masataka Sato
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Susumu Katsushika
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroki Shinohara
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Norifumi Takeda
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Katsuhito Fujiu
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- Department of Advanced Cardiology, The University of Tokyo, Tokyo, Japan
| | - Masao Daimon
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
- Department of Clinical Laboratory, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroshi Akazawa
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, The University of Tokyo Hospital, Tokyo, Japan
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3
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Takase B, Ikeda T, Shimizu W, Abe H, Aiba T, Chinushi M, Koba S, Kusano K, Niwano S, Takahashi N, Takatsuki S, Tanno K, Watanabe E, Yoshioka K, Amino M, Fujino T, Iwasaki Y, Kohno R, Kinoshita T, Kurita Y, Masaki N, Murata H, Shinohara T, Yada H, Yodogawa K, Kimura T, Kurita T, Nogami A, Sumitomo N. JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia. J Arrhythm 2024; 40:655-752. [PMID: 39139890 PMCID: PMC11317726 DOI: 10.1002/joa3.13052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 04/22/2024] [Indexed: 08/15/2024] Open
Affiliation(s)
| | - Takanori Ikeda
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Wataru Shimizu
- Department of Cardiovascular MedicineNippon Medical School
| | - Haruhiko Abe
- Department of Heart Rhythm ManagementUniversity of Occupational and Environmental HealthJapan
| | - Takeshi Aiba
- Department of Clinical Laboratory Medicine and GeneticsNational Cerebral and Cardiovascular Center
| | | | - Shinji Koba
- Division of Cardiology, Department of MedicineShowa University School of Medicine
| | - Kengo Kusano
- Department of Cardiovascular MedicineNational Cerebral and Cardiovascular Center
| | - Shinichi Niwano
- Department of Cardiovascular MedicineKitasato University School of Medicine
| | - Naohiko Takahashi
- Department of Cardiology and Clinical Examination, Faculty of MedicineOita University
| | | | - Kaoru Tanno
- Cardiovascular Center, Cardiology DivisionShowa University Koto‐Toyosu Hospital
| | - Eiichi Watanabe
- Division of Cardiology, Department of Internal MedicineFujita Health University Bantane Hospital
| | | | - Mari Amino
- Department of CardiologyTokai University School of Medicine
| | - Tadashi Fujino
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Yu‐ki Iwasaki
- Department of Cardiovascular MedicineNippon Medical School
| | - Ritsuko Kohno
- Department of Heart Rhythm ManagementUniversity of Occupational and Environmental HealthJapan
| | - Toshio Kinoshita
- Department of Cardiovascular MedicineToho University Faculty of Medicine
| | - Yasuo Kurita
- Cardiovascular Center, Mita HospitalInternational University of Health and Welfare
| | - Nobuyuki Masaki
- Department of Intensive Care MedicineNational Defense Medical College
| | | | - Tetsuji Shinohara
- Department of Cardiology and Clinical Examination, Faculty of MedicineOita University
| | - Hirotaka Yada
- Department of CardiologyInternational University of Health and Welfare Mita Hospital
| | - Kenji Yodogawa
- Department of Cardiovascular MedicineNippon Medical School
| | - Takeshi Kimura
- Cardiovascular MedicineKyoto University Graduate School of Medicine
| | | | - Akihiko Nogami
- Department of Cardiology, Faculty of MedicineUniversity of Tsukuba
| | - Naokata Sumitomo
- Department of Pediatric CardiologySaitama Medical University International Medical Center
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4
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Cai YQ, Gong DX, Tang LY, Cai Y, Li HJ, Jing TC, Gong M, Hu W, Zhang ZW, Zhang X, Zhang GW. Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions. J Med Internet Res 2024; 26:e47645. [PMID: 38869157 PMCID: PMC11316160 DOI: 10.2196/47645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 10/30/2023] [Accepted: 06/12/2024] [Indexed: 06/14/2024] Open
Abstract
In recent years, there has been explosive development in artificial intelligence (AI), which has been widely applied in the health care field. As a typical AI technology, machine learning models have emerged with great potential in predicting cardiovascular diseases by leveraging large amounts of medical data for training and optimization, which are expected to play a crucial role in reducing the incidence and mortality rates of cardiovascular diseases. Although the field has become a research hot spot, there are still many pitfalls that researchers need to pay close attention to. These pitfalls may affect the predictive performance, credibility, reliability, and reproducibility of the studied models, ultimately reducing the value of the research and affecting the prospects for clinical application. Therefore, identifying and avoiding these pitfalls is a crucial task before implementing the research. However, there is currently a lack of a comprehensive summary on this topic. This viewpoint aims to analyze the existing problems in terms of data quality, data set characteristics, model design, and statistical methods, as well as clinical implications, and provide possible solutions to these problems, such as gathering objective data, improving training, repeating measurements, increasing sample size, preventing overfitting using statistical methods, using specific AI algorithms to address targeted issues, standardizing outcomes and evaluation criteria, and enhancing fairness and replicability, with the goal of offering reference and assistance to researchers, algorithm developers, policy makers, and clinical practitioners.
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Affiliation(s)
- Yu-Qing Cai
- The First Hospital of China Medical University, Shenyang, China
| | - Da-Xin Gong
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
| | - Li-Ying Tang
- The First Hospital of China Medical University, Shenyang, China
| | - Yue Cai
- The First Hospital of China Medical University, Shenyang, China
| | - Hui-Jun Li
- Shenyang Medical & Film Science and Technology Co, Ltd, Shenyang, China
| | - Tian-Ci Jing
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
| | | | - Wei Hu
- Bayi Orthopedic Hospital, Chengdu, China
| | - Zhen-Wei Zhang
- China Rongtong Medical & Healthcare Co, Ltd, Chengdu, China
| | - Xingang Zhang
- Department of Cardiology, The First Hospital of China Medical University, Shenyang, China
| | - Guang-Wei Zhang
- Smart Hospital Management Department, The First Hospital of China Medical University, Shenyang, China
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5
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Zhao K, Zhu Y, Chen X, Yang S, Yan W, Yang K, Song Y, Cui C, Xu X, Zhu Q, Cui ZX, Yin G, Cheng H, Lu M, Liang D, Shi K, Zhao L, Liu H, Zhang J, Chen L, Prasad SK, Zhao S, Zheng H. Machine Learning in Hypertrophic Cardiomyopathy: Nonlinear Model From Clinical and CMR Features Predicting Cardiovascular Events. JACC Cardiovasc Imaging 2024:S1936-878X(24)00183-9. [PMID: 39001729 DOI: 10.1016/j.jcmg.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Revised: 04/02/2024] [Accepted: 04/19/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND The cumulative burden of hypertrophic cardiomyopathy (HCM) is significant, with a noteworthy percentage (10%-15%) of patients with HCM per year experiencing major adverse cardiovascular events (MACEs). A current risk stratification scheme for HCM had only limited accuracy in predicting sudden cardiac death (SCD) and failed to account for a broader spectrum of adverse cardiovascular events and cardiac magnetic resonance (CMR) parameters. OBJECTIVES This study sought to develop and evaluate a machine learning (ML) framework that integrates CMR imaging and clinical characteristics to predict MACEs in patients with HCM. METHODS A total of 758 patients with HCM (67% male; aged 49 ± 14 years) who were admitted between 2010 and 2017 from 4 medical centers were included. The ML model was built on the internal discovery cohort (533 patients with HCM, admitted to Fuwai Hospital, Beijing, China) by using the light gradient-boosting machine and internally evaluated using cross-validation. The external test cohort consisted of 225 patients with HCM from 3 medical centers. A total of 14 CMR imaging features (strain and late gadolinium enhancement [LGE]) and 23 clinical variables were evaluated and used to inform the ML model. MACEs included a composite of arrhythmic events, SCD, heart failure, and atrial fibrillation-related stroke. RESULTS MACEs occurred in 191 (25%) patients over a median follow-up period of 109.0 months (Q1-Q3: 73.0-118.8 months). Our ML model achieved areas under the curve (AUCs) of 0.830 and 0.812 (internally and externally, respectively). The model outperformed the classic HCM Risk-SCD model, with significant improvement (P < 0.001) of 22.7% in the AUC. Using the cubic spline analysis, the study showed that the extent of LGE and the impairment of global radial strain (GRS) and global circumferential strain (GCS) were nonlinearly correlated with MACEs: an elevated risk of adverse cardiovascular events was observed when these parameters reached the high enough second tertiles (11.6% for LGE, 25.8% for GRS, -17.3% for GCS). CONCLUSIONS ML-empowered risk stratification using CMR and clinical features enabled accurate MACE prediction beyond the classic HCM Risk-SCD model. In addition, the nonlinear correlation between CMR features (LGE and left ventricular pressure gradient) and MACEs uncovered in this study provides valuable insights for the clinical assessment and management of HCM.
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Affiliation(s)
- Kankan Zhao
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China
| | - Xiuyu Chen
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shujuan Yang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Weipeng Yan
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kai Yang
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanyan Song
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chen Cui
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xi Xu
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qingyong Zhu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China
| | - Zhuo-Xu Cui
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China
| | - Gang Yin
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huaibin Cheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China
| | - Minjie Lu
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China
| | - Ke Shi
- Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Lei Zhao
- Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
| | - Hui Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
| | - Jiayin Zhang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Liang Chen
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sanjay K Prasad
- Cardiovascular Magnetic Resonance Unit, Royal Brompton Hospital, London, United Kingdom; National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Shihua Zhao
- State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Hairong Zheng
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, China.
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6
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Whitman M, Tilley P, Padayachee C, Jenkins C, Challa P. Energy wavelet signal processed ECG and standard 12 lead ECG: Diagnosis of early diastolic dysfunction. J Electrocardiol 2024; 85:1-6. [PMID: 38762938 DOI: 10.1016/j.jelectrocard.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 01/23/2024] [Accepted: 05/03/2024] [Indexed: 05/21/2024]
Abstract
BACKGROUND Left ventricular (LV) diastolic dysfunction (LVDD) is the result of impaired LV relaxation and identifies those at risk of developing heart failure. Echocardiography has been used as the gold standard to identify early LVDD. The signal processed electrocardiogram (hsECG) has demonstrated effectiveness to detect early LVDD. Whether or not the standard 12‑lead electrocardiogram (ECG) can accurately predict early LVDD is not known. METHODS A standard 12‑lead ECG including signal processing (hsECG) was performed in 569 patients. Patients with atrial fibrillation, bundle branch block, pre-excitation, left ventricular hypertrophy or known cardiovascular disease were excluded, leaving 464 examinations for analysis. Early LVDD was diagnosed by established methods using echocardiography. Repolarization abnormalities (T wave discordance) in V1, V6, I and aVL and the hsECG were compared to the echocardiographic findings to establish diagnostic accuracy. RESULTS A total of 84 (18.1%) patients were diagnosed with early LVDD. A combination of a borderline or abnormal finding on the hsECG produced the best diagnostic model (sensitivity 84.5%, specificity 47.9%). The best performing ECG lead was V1 with a sensitivity of 38.1% and specificity of 92.1%. Regression analysis demonstrated increasing age and V1 to be predictive of LVDD. CONCLUSIONS The hsECG displayed reasonable ability to detect early LVDD. Other than V1, repolarization abnormalities on the standard 12‑lead ECG did not. While lead V1 showed promise in detecting LVDD, whether this or any other simple ECG variable can predict future LVDD would be of further interest.
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Affiliation(s)
- Mark Whitman
- Cardiac Investigations Unit, Logan Hospital, Meadowbrook, Australia; School of Human Movement and Nutrition Sciences, University of Queensland, Brisbane, Australia.
| | - Prue Tilley
- Cardiac Investigations Unit, Logan Hospital, Meadowbrook, Australia
| | | | - Carly Jenkins
- Cardiac Investigations Unit, Logan Hospital, Meadowbrook, Australia
| | - Prasad Challa
- Division of Cardiology, Logan Hospital, Meadowbrook, Australia
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7
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Ma R, Zhao J, Wen Z, Qin Y, Yu Z, Yuan J, Zhang Y, Wang A, Li C, Li H, Chen Y, Han F, Zhao Y, Sun S, Ning X. Machine learning for the prediction of delirium in elderly intensive care unit patients. Eur Geriatr Med 2024:10.1007/s41999-024-01012-y. [PMID: 38937402 DOI: 10.1007/s41999-024-01012-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
PURPOSE This study aims to develop and validate a prediction model for delirium in elderly ICU patients and help clinicians identify high-risk patients at the early stage. METHODS Patients admitted to ICU for at least 24 h and using the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (76,943 ICU stays from 2008 to 2019) were considered. Patients with a positive delirium test in the first 24 h and under 65 years of age were excluded. Two prediction models, machine learning extreme gradient boosting (XGBoost) and logistic regression (LR) model, were developed and validated to predict the onset of delirium. RESULTS Of the 18,760 patients included in the analysis, 3463(18.5%) were delirium positive. A total of 22 significant predictors were selected by LASSO regression. The XGBoost model demonstrated superior performance over the LR model, with the Area Under the Receiver Operating Characteristic (AUC) values of 0.853 (95% confidence interval [CI] 0.846-0.861) and 0.831 (95% CI 0.815-0.847) in the training and testing datasets, respectively. Moreover, the XGBoost model outperformed the LR model in both calibration and clinical utility. The top five predictors associated with the onset of delirium were sequential organ failure assessment (SOFA), infection, minimum platelets, maximum systolic blood pressure (SBP), and maximum temperature. CONCLUSION The XGBoost model demonstrated good predictive performance for delirium among elderly ICU patients, thus assisting clinicians in identifying high-risk patients at the early stage and implementing targeted interventions to improve outcome.
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Affiliation(s)
- Rui Ma
- Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Jin Zhao
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Ziying Wen
- Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Yunlong Qin
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
- Department of Nephrology, Bethune International Peace Hospital, Shijiazhuang, China
| | - Zixian Yu
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Jinguo Yuan
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Yumeng Zhang
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Anjing Wang
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Cui Li
- Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Huan Li
- Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Yang Chen
- Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Fengxia Han
- Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China
| | - Yueru Zhao
- Medicine School of Xi'an Jiaotong University, Xi'an, China
| | - Shiren Sun
- Department of Nephrology, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China.
| | - Xiaoxuan Ning
- Department of Geriatrics, Xijing Hospital, Fourth Military Medical University, No. 127 Chang Le West Road, Xi'an, 710032, Shaanxi, China.
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8
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Zheng C, Cai A, Wang X, Qiu J, Song Q, Gu R, Cao X, Tian Y, Hu Z, Fonarow GC, Lip GY, Wang Z, Feng Y. Prognostic implications of heart failure stages among Chinese community populations: insight from a nationwide population-based study. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 46:101072. [PMID: 38706523 PMCID: PMC11067477 DOI: 10.1016/j.lanwpc.2024.101072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 03/25/2024] [Accepted: 04/08/2024] [Indexed: 05/07/2024]
Abstract
Background In light of high burden of heart failure (HF) in China, studies of prognostic implication of HF stages are important. We aimed to evaluate the relationship between HF stages and mortality risk in Chinese community populations. Methods Nationwide representative populations aged ≥35 years (n = 23,284, mean age 56.9 years, women 53.2%) were enrolled from 2012 to 2016. According to the international HF guidelines, participants were divided into stage A, B and C, and those who did not qualify these stages were categorized as apparently-healthy group. Association between HF stages and all-cause, cardiovascular [CV] and non-CV death was evaluated using multivariable-adjusted Cox proportional regression analysis. Findings During a median follow-up of 4.7 years (109,902.8 person-years), 1314 deaths occurred. Age-adjusted incidence rate of all-cause death was 5.3 in apparently-healthy, 7.8 in stage A, 8.6 in stage B and 24.6 in stage C groups per 1000 person-years. In reference to apparently-healthy group, adjusted hazard ratio for all-cause death was 1.90 (95% CI: 1.47-2.45), 2.43 (95% CI: 1.89-3.13) and 6.40 (95% CI: 4.56-8.99) for stage A, B and C. Advancing HF stages were associated with increasing risks for all-cause, CV and non-CV death (P-trend <0.05). For all-cause death, population attributable fraction due to stage A, B and C were 21.2%, 33.4% and 4.9%, accounting for 1,933,385, 3,045,993 and 446,867 deaths in China in 2018. Interpretation Advancing HF stages were associated with increasing risk mortality. Development and implementation of early screening and targeted interventions are urgently needed to reduce HF burdens in China. Funding This work was supported by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (grant 2017-I2M-1-004), the Projects in the Chinese National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (No.: 2011BAI11B01), and the Project Entrusted by the National Health Commission of the People's Republic of China (NHC2020-609).
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Affiliation(s)
- Congyi Zheng
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Anping Cai
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China
| | - Xin Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Jiayuan Qiu
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, 201701, China
| | - Qingjie Song
- Department of Epidemiology, School of Public Health, Medical College of Soochow University, Suzhou, 201701, China
| | - Runqing Gu
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Xue Cao
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Yixin Tian
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Zhen Hu
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Gregg C. Fonarow
- Division of Cardiology, Department of Medicine, Geffen School of Medicine at UCLA, Los Angeles, CA, United States
| | - Gregory Y.H. Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Zengwu Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 102308, China
| | - Yingqing Feng
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, Guangzhou, 510080, China
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9
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Jia Y, Li Y, Luosang G, Wang J, Peng G, Pu X, Jiang W, Li W, Zhao Z, Peng Y, Feng Y, Wei J, Xu Y, Liu X, Yi Z, Chen M. Electrocardiogram-based prediction of conduction disturbances after transcatheter aortic valve replacement with convolutional neural network. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:219-228. [PMID: 38774374 PMCID: PMC11104474 DOI: 10.1093/ehjdh/ztae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 12/21/2023] [Accepted: 01/06/2024] [Indexed: 05/24/2024]
Abstract
Aims Permanent pacemaker implantation and left bundle branch block are common complications after transcatheter aortic valve replacement (TAVR) and are associated with impaired prognosis. This study aimed to develop an artificial intelligence (AI) model for predicting conduction disturbances after TAVR using pre-procedural 12-lead electrocardiogram (ECG) images. Methods and results We collected pre-procedural 12-lead ECGs of patients who underwent TAVR at West China Hospital between March 2016 and March 2022. A hold-out testing set comprising 20% of the sample was randomly selected. We developed an AI model using a convolutional neural network, trained it using five-fold cross-validation and tested it on the hold-out testing cohort. We also developed and validated an enhanced model that included additional clinical features. After applying exclusion criteria, we included 1354 ECGs of 718 patients in the study. The AI model predicted conduction disturbances in the hold-out testing cohort with an area under the curve (AUC) of 0.764, accuracy of 0.743, F1 score of 0.752, sensitivity of 0.876, and specificity of 0.624, based solely on pre-procedural ECG images. The performance was better than the Emory score (AUC = 0.704), as well as the logistic (AUC = 0.574) and XGBoost (AUC = 0.520) models built with previously identified high-risk ECG patterns. After adding clinical features, there was an increase in the overall performance with an AUC of 0.779, accuracy of 0.774, F1 score of 0.776, sensitivity of 0.794, and specificity of 0.752. Conclusion Artificial intelligence-enhanced ECGs may offer better predictive value than traditionally defined high-risk ECG patterns.
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Affiliation(s)
- Yuheng Jia
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yiming Li
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Gaden Luosang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
- Department of Information Science and Technology, Tibet University, No.10 Zangda East Road, Lhasa 850000, Tibet, P. R. China
| | - Jianyong Wang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Gang Peng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Xingzhou Pu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Weili Jiang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Wenjian Li
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Zhengang Zhao
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yong Peng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yuan Feng
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Jiafu Wei
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Yuanning Xu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Xingbin Liu
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
| | - Zhang Yi
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, No.24 South Section 1, Yihuan Road, Chengdu 610065, P. R. China
| | - Mao Chen
- Department of Cardiology, West China Hospital, Sichuan University, No.37 Guoxue Street, Chengdu 610041, P. R. China
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10
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Soh CH, de Sá AGC, Potter E, Halabi A, Ascher DB, Marwick TH. Use of the energy waveform electrocardiogram to detect subclinical left ventricular dysfunction in patients with type 2 diabetes mellitus. Cardiovasc Diabetol 2024; 23:91. [PMID: 38448993 PMCID: PMC10918872 DOI: 10.1186/s12933-024-02141-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 01/22/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Recent guidelines propose N-terminal pro-B-type natriuretic peptide (NT-proBNP) for recognition of asymptomatic left ventricular (LV) dysfunction (Stage B Heart Failure, SBHF) in type 2 diabetes mellitus (T2DM). Wavelet Transform based signal-processing transforms electrocardiogram (ECG) waveforms into an energy distribution waveform (ew)ECG, providing frequency and energy features that machine learning can use as additional inputs to improve the identification of SBHF. Accordingly, we sought whether machine learning model based on ewECG features was superior to NT-proBNP, as well as a conventional screening tool-the Atherosclerosis Risk in Communities (ARIC) HF risk score, in SBHF screening among patients with T2DM. METHODS Participants in two clinical trials of SBHF (defined as diastolic dysfunction [DD], reduced global longitudinal strain [GLS ≤ 18%] or LV hypertrophy [LVH]) in T2DM underwent 12-lead ECG with additional ewECG feature and echocardiography. Supervised machine learning was adopted to identify the optimal combination of ewECG extracted features for SBHF screening in 178 participants in one trial and tested in 97 participants in the other trial. The accuracy of the ewECG model in SBHF screening was compared with NT-proBNP and ARIC HF. RESULTS SBHF was identified in 128 (72%) participants in the training dataset (median 72 years, 41% female) and 64 (66%) in the validation dataset (median 70 years, 43% female). Fifteen ewECG features showed an area under the curve (AUC) of 0.81 (95% CI 0.787-0.794) in identifying SBHF, significantly better than both NT-proBNP (AUC 0.56, 95% CI 0.44-0.68, p < 0.001) and ARIC HF (AUC 0.67, 95%CI 0.56-0.79, p = 0.002). ewECG features were also led to robust models screening for DD (AUC 0.74, 95% CI 0.73-0.74), reduced GLS (AUC 0.76, 95% CI 0.73-0.74) and LVH (AUC 0.90, 95% CI 0.88-0.89). CONCLUSIONS Machine learning based modelling using additional ewECG extracted features are superior to NT-proBNP and ARIC HF in SBHF screening among patients with T2DM, providing an alternative HF screening strategy for asymptomatic patients and potentially act as a guidance tool to determine those who required echocardiogram to confirm diagnosis. Trial registration LEAVE-DM, ACTRN 12619001393145 and Vic-ELF, ACTRN 12617000116325.
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Affiliation(s)
- Cheng Hwee Soh
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Alex G C de Sá
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia
- Systems and Computational Biology, Bio21 Institute, Parkville, Australia
| | - Elizabeth Potter
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
| | - Amera Halabi
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia
| | - David B Ascher
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia
- Systems and Computational Biology, Bio21 Institute, Parkville, Australia
| | - Thomas H Marwick
- Imaging Research Laboratory, Baker Heart and Diabetes Institute, PO Box 6492, Melbourne, VIC, 3004, Australia.
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia.
- Menzies Institute for Medical Research, Hobart, Australia.
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11
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Chang A, Wu X, Liu K. Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms. BIOPHYSICS REVIEWS 2024; 5:011304. [PMID: 38559589 PMCID: PMC10978053 DOI: 10.1063/5.0176850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 03/01/2024] [Indexed: 04/04/2024]
Abstract
A key strength of echocardiography lies in its integration of comprehensive spatiotemporal cardiac imaging data in real-time, to aid frontline or bedside patient risk stratification and management. Nonetheless, its acquisition, processing, and interpretation are known to all be subject to heterogeneity from its reliance on manual and subjective human tracings, which challenges workflow and protocol standardization and final interpretation accuracy. In the era of advanced computational power, utilization of machine learning algorithms for big data analytics in echocardiography promises reduction in cost, cognitive errors, and intra- and inter-observer variability. Novel spatiotemporal deep learning (DL) models allow the integration of temporal arm information based on unlabeled pixel echocardiographic data for convolution of an adaptive semantic spatiotemporal calibration to construct personalized 4D heart meshes, assess global and regional cardiac function, detect early valve pathology, and differentiate uncommon cardiovascular disorders. Meanwhile, data visualization on spatiotemporal DL prediction models helps extract latent temporal imaging features to develop advanced imaging biomarkers in early disease stages and advance our understanding of pathophysiology to support the development of personalized prevention or treatment strategies. Since portable echocardiograms have been increasingly used as point-of-care imaging tools to aid rural care delivery, the application of these new spatiotemporal DL techniques show the potentials in streamlining echocardiographic acquisition, processing, and data analysis to improve workflow standardization and efficiencies, and provide risk stratification and decision supporting tools in real-time, to prompt the building of new imaging diagnostic networks to enhance rural healthcare engagement.
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Affiliation(s)
- Amanda Chang
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Iowa, Iowa City, Iowa 52242, USA
| | - Xiaodong Wu
- Department of Electrical and Computer Engineering, College of Engineering, University of Iowa, Iowa City, Iowa 52242, USA
| | - Kan Liu
- Division of Cardiology, Department of Internal Medicine, Washington University in St. Louis, St. Louis, Missouri 63110, USA
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12
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Makimoto H, Kohro T. Adopting artificial intelligence in cardiovascular medicine: a scoping review. Hypertens Res 2024; 47:685-699. [PMID: 37907600 DOI: 10.1038/s41440-023-01469-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 09/03/2023] [Accepted: 09/26/2023] [Indexed: 11/02/2023]
Abstract
Recent years have witnessed significant transformations in cardiovascular medicine, driven by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to capture the breadth of AI applications within cardiovascular science. Employing a structured approach, we sourced relevant articles from PubMed, with an emphasis on journals encompassing general cardiology and digital medicine. We applied filters to highlight cardiovascular articles published in journals focusing on general internal medicine, cardiology and digital medicine, thereby identifying the prevailing trends in the field. Following a comprehensive full-text screening, a total of 140 studies were identified. Over the preceding 5 years, cardiovascular medicine's interplay with AI has seen an over tenfold augmentation. This expansive growth encompasses multiple cardiovascular subspecialties, including but not limited to, general cardiology, ischemic heart disease, heart failure, and arrhythmia. Deep learning emerged as the predominant methodology. The majority of AI endeavors in this domain have been channeled toward enhancing diagnostic and prognostic capabilities, utilizing resources such as hospital datasets, electrocardiograms, and echocardiography. A significant uptrend was observed in AI's application for omics data analysis. However, a clear gap persists in AI's full-scale integration into the clinical decision-making framework. AI, particularly deep learning, has demonstrated robust applications across cardiovascular subspecialties, indicating its transformative potential in this field. As we continue on this trajectory, ensuring the alignment of technological progress with medical ethics becomes crucial. The abundant digital health data today further accentuates the need for meticulous systematic reviews, tailoring them to each cardiovascular subspecialty.
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Affiliation(s)
- Hisaki Makimoto
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan.
| | - Takahide Kohro
- Data Science Center/Cardiovascular Center, Jichi Medical University, Shimotsuke, Japan
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13
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Hanneman K, Playford D, Dey D, van Assen M, Mastrodicasa D, Cook TS, Gichoya JW, Williamson EE, Rubin GD. Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e296-e311. [PMID: 38193315 DOI: 10.1161/cir.0000000000001202] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.
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14
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Lee E, Ito S, Miranda WR, Lopez-Jimenez F, Kane GC, Asirvatham SJ, Noseworthy PA, Friedman PA, Carter RE, Borlaug BA, Attia ZI, Oh JK. Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure. NPJ Digit Med 2024; 7:4. [PMID: 38182738 PMCID: PMC10770308 DOI: 10.1038/s41746-023-00993-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024] Open
Abstract
Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645-1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298-1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.
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Affiliation(s)
- Eunjung Lee
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Saki Ito
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - William R Miranda
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Garvan C Kane
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | | | - Paul A Friedman
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Rickey E Carter
- Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA
| | - Barry A Borlaug
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Zachi I Attia
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Jae K Oh
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
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15
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Xie M, Zhu S, Liu G, Wu Y, Zhou W, Yu D, Wan J, Xing S, Wang S, Gan L, Li G, Chang D, Lai H, Liu N, Zhu P. A Novel Quantitative Electrocardiography Strategy Reveals the Electroinhibitory Effect of Tamoxifen on the Mouse Heart. J Cardiovasc Transl Res 2023; 16:1232-1248. [PMID: 37155136 DOI: 10.1007/s12265-023-10395-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/26/2023] [Indexed: 05/10/2023]
Abstract
Tamoxifen, a selective estrogen receptor modulator, was initially used to treat cancer in women and more recently to induce conditional gene editing in rodent hearts. However, little is known about the baseline biological effects of tamoxifen on the myocardium. In order to clarify the short-term effects of tamoxifen on cardiac electrophysiology of myocardium, we applied a single-chest-lead quantitative method and analyzed the short-term electrocardiographic phenotypes induced by tamoxifen in the heart of adult female mice. We found that tamoxifen prolonged the PP interval and caused a decreased heartbeat, and further induced atrioventricular block by gradually prolonging the PR interval. Further correlation analysis suggested that tamoxifen had a synergistic and dose-independent inhibition on the time course of the PP interval and PR interval. This prolongation of the critical time course may represent a tamoxifen-specific ECG excitatory-inhibitory mechanism, leading to a reduction in the number of supraventricular action potentials and thus bradycardia. Segmental reconstructions showed that tamoxifen induced a decrease in the conduction velocity of action potentials throughout the atria and parts of the ventricles, resulting in a flattening of the P wave and R wave. In addition, we detected the previously reported prolongation of the QT interval, which may be due to a prolonged duration of the ventricular repolarizing T wave rather than the depolarizing QRS complex. Our study highlights that tamoxifen can produce patterning alternations in the cardiac conduction system, including the formation of inhibitory electrical signals with reduced conduction velocity, implying its involvement in the regulation of myocardial ion transport and the mediation of arrhythmias. A Novel Quantitative Electrocardiography Strategy Reveals the Electroinhibitory Effect of Tamoxifen on the Mouse Heart(Figure 9). A working model of tamoxifen producing acute electrical disturbances in the myocardium. SN, sinus node; AVN, atrioventricular node; RA, right atrium; LA, left atrium; RV, right ventricle; LV, left ventricle.
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Affiliation(s)
- Ming Xie
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Shuoji Zhu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
- University of Tokyo, Tokyo, 113-8666, Japan
| | - Gang Liu
- Department of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yijin Wu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Wenkai Zhou
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Dingdang Yu
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Jinkai Wan
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Shenghui Xing
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Siqing Wang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Lin Gan
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Ge Li
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China
| | - Dehua Chang
- University of Tokyo Hospital Department of Cell Therapy in Regenerative Medicine, Tokyo, 113-8666, Japan.
| | - Hao Lai
- Department of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
| | - Nanbo Liu
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China.
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China.
| | - Ping Zhu
- Department of Cardiac Surgery, School of Medicine, South China University of Technology, Guangzhou, 510006, Guangdong, China.
- Department of Cardiac Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510100, Guangdong, China.
- Guangdong Provincial Key Laboratory of Pathogenesis, Targeted Prevention and Treatment of Heart Disease, and Guangzhou Key Laboratory of Cardiac Pathogenesis and Prevention, Guangzhou, 510100, Guangdong, China.
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16
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Khan MS, Arshad MS, Greene SJ, Van Spall HGC, Pandey A, Vemulapalli S, Perakslis E, Butler J. Artificial intelligence and heart failure: A state-of-the-art review. Eur J Heart Fail 2023; 25:1507-1525. [PMID: 37560778 DOI: 10.1002/ejhf.2994] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 08/06/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023] Open
Abstract
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding the pathophysiology and phenotypes of patients with HF and preserved ejection fraction, and the challenges related to integrating a large amount of disparate information available for risk stratification and management of these patients. Risk prediction algorithms based on artificial intelligence (AI) may have superior predictive ability compared to traditional methods in certain instances. AI algorithms can play a pivotal role in the evolution of HF care by facilitating clinical decision making to overcome various challenges such as allocation of treatment to patients who are at highest risk or are more likely to benefit from therapies, prediction of adverse outcomes, and early identification of patients with subclinical disease or worsening HF. With the ability to integrate and synthesize large amounts of data with multidimensional interactions, AI algorithms can supply information with which physicians can improve their ability to make timely and better decisions. In this review, we provide an overview of the AI algorithms that have been developed for establishing early diagnosis of HF, phenotyping HF with preserved ejection fraction, and stratifying HF disease severity. This review also discusses the challenges in clinical deployment of AI algorithms in HF, and the potential path forward for developing future novel learning-based algorithms to improve HF care.
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Affiliation(s)
| | | | - Stephen J Greene
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - Harriette G C Van Spall
- Department of Medicine and Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Ambarish Pandey
- Canada Population Health Research Institute, Hamilton, ON, Canada
- Division of Cardiology, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - Sreekanth Vemulapalli
- Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | | | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Baylor Scott and White Research Institute, Dallas, TX, USA
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17
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Darmawahyuni A, Nurmaini S, Rachmatullah MN, Avi PP, Teguh SBP, Sapitri AI, Tutuko B, Firdaus F. Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm. BMC Med Inform Decis Mak 2023; 23:139. [PMID: 37507698 PMCID: PMC10375607 DOI: 10.1186/s12911-023-02233-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
BACKGROUND Signal delineation of a standard 12-lead electrocardiogram (ECG) is a decisive step for retrieving complete information and extracting signal characteristics for each lead in cardiology clinical practice. However, it is arduous to manually assess the leads, as a variety of signal morphological variations in each lead have potential defects in recording, noise, or irregular heart rhythm/beat. METHOD A computer-aided deep-learning algorithm is considered a state-of-the-art delineation model to classify ECG waveform and boundary in terms of the P-wave, QRS-complex, and T-wave and indicated the satisfactory result. This study implemented convolution layers as a part of convolutional neural networks for automated feature extraction and bidirectional long short-term memory as a classifier. For beat segmentation, we have experimented beat-based and patient-based approach. RESULTS The empirical results using both beat segmentation approaches, with a total of 14,588 beats were showed that our proposed model performed excellently well. All performance metrics above 95% and 93%, for beat-based and patient-based segmentation, respectively. CONCLUSIONS This is a significant step towards the clinical pertinency of automated 12-lead ECG delineation using deep learning.
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Affiliation(s)
- Annisa Darmawahyuni
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia.
| | - Muhammad Naufal Rachmatullah
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Prazna Paramitha Avi
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Samuel Benedict Putra Teguh
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Bambang Tutuko
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
| | - Firdaus Firdaus
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia
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18
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Yamada S, Ko T, Katagiri M, Morita H, Komuro I. Recent Advances in Translational Research for Heart Failure in Japan. J Card Fail 2023; 29:931-938. [PMID: 37321698 DOI: 10.1016/j.cardfail.2022.11.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 11/13/2022] [Accepted: 11/14/2022] [Indexed: 06/17/2023]
Abstract
Despite decades of intensive research and therapeutic development, heart failure remains a leading cause of death worldwide. However, recent advances in several basic and translational research fields, such as genomic analysis and single-cell analysis, have increased the possibility of developing novel diagnostic approaches to heart failure. Most cardiovascular diseases that predispose individuals to heart failure are caused by genetic and environmental factors. It follows that genomic analysis can contribute to the diagnosis and prognostic stratification of patients with heart failure. In addition, single-cell analysis has shown great potential for unveiling the pathogenesis and/or pathophysiology and for discovering novel therapeutic targets for heart failure. Here, we summarize the recent advances in translational research on heart failure in Japan, based mainly on our studies.
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Affiliation(s)
- Shintaro Yamada
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Toshiyuki Ko
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mikako Katagiri
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiroyuki Morita
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Issei Komuro
- Department of Cardiovascular Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Cardiovascular Medicine, Graduate School of Medicine, International University of Health and Welfare, Tokyo, Japan.
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Phonocardiogram transfer learning-based CatBoost model for diastolic dysfunction identification using multiple domain-specific deep feature fusion. Comput Biol Med 2023; 156:106707. [PMID: 36871337 DOI: 10.1016/j.compbiomed.2023.106707] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 02/11/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Left ventricular diastolic dyfunction detection is particularly important in cardiac function screening. This paper proposed a phonocardiogram (PCG) transfer learning-based CatBoost model to detect diastolic dysfunction noninvasively. The Short-Time Fourier Transform (STFT), Mel Frequency Cepstral Coefficients (MFCCs), S-transform and gammatonegram were utilized to perform four different representations of spectrograms for learning the representative patterns of PCG signals in two-dimensional image modality. Then, four pre-trained convolutional neural networks (CNNs) such as VGG16, Xception, ResNet50 and InceptionResNetv2 were employed to extract multiple domain-specific deep features from PCG spectrograms using transfer learning, respectively. Further, principal component analysis and linear discriminant analysis (LDA) were applied to different feature subsets, respectively, and then these different selected features are fused and fed into CatBoost for classification and performance comparison. Finally, three typical machine learning classifiers such as multilayer perceptron, support vector machine and random forest were employed to compared with CatBoost. The hyperparameter optimization of the investigated models was determined through grid search. The visualized result of the global feature importance showed that deep features extracted from gammatonegram by ResNet50 contributed most to classification. Overall, the proposed multiple domain-specific feature fusion based CatBoost model with LDA achieved the best performance with an area under the curve of 0.911, accuracy of 0.882, sensitivity of 0.821, specificity of 0.927, F1-score of 0.892 on the testing set. The PCG transfer learning-based model developed in this study could aid in diastolic dysfunction detection and could contribute to non-invasive evaluation of diastolic function.
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20
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Vaid A, Argulian E, Lerakis S, Beaulieu-Jones BK, Krittanawong C, Klang E, Lampert J, Reddy VY, Narula J, Nadkarni GN, Glicksberg BS. Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction. COMMUNICATIONS MEDICINE 2023; 3:24. [PMID: 36788316 PMCID: PMC9929085 DOI: 10.1038/s43856-023-00240-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 01/09/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND Aortic Stenosis and Mitral Regurgitation are common valvular conditions representing a hidden burden of disease within the population. The aim of this study was to develop and validate deep learning-based screening and diagnostic tools that can help guide clinical decision making. METHODS In this multi-center retrospective cohort study, we acquired Transthoracic Echocardiogram reports from five Mount Sinai hospitals within New York City representing a demographically diverse cohort of patients. We developed a Natural Language Processing pipeline to extract ground-truth labels about valvular status and paired these to Electrocardiograms (ECGs). We developed and externally validated deep learning models capable of detecting valvular disease, in addition to considering scenarios of clinical deployment. RESULTS We use 617,338 ECGs paired to transthoracic echocardiograms from 123,096 patients to develop a deep learning model for detection of Mitral Regurgitation. Area Under Receiver Operating Characteristic curve (AUROC) is 0.88 (95% CI:0.88-0.89) in internal testing, and 0.81 (95% CI:0.80-0.82) in external validation. To develop a model for detection of Aortic Stenosis, we use 617,338 Echo-ECG pairs for 128,628 patients. AUROC is 0.89 (95% CI: 0.88-0.89) in internal testing, going to 0.86 (95% CI: 0.85-0.87) in external validation. The model's performance increases leading up to the time of the diagnostic echo, and it performs well in validation against requirement of Transcatheter Aortic Valve Replacement procedures. CONCLUSIONS Deep learning based tools can increase the amount of information extracted from ubiquitous investigations such as the ECG. Such tools are inexpensive, can help in earlier disease detection, and potentially improve prognosis.
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Affiliation(s)
- Akhil Vaid
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stamatios Lerakis
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Section of Biomedical Data Science, Department of Medicine, University of Chicago, Chicago, IL, USA
| | | | - Eyal Klang
- Sheba Medical Center, Department of Diagnostic Imaging, Tel Hashomer, Israel
- Sackler Medical School, Tel Aviv University, Tel Aviv, 52621, Israel
| | - Joshua Lampert
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vivek Y Reddy
- Helmsley Electrophysiology Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Girish N Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Division of Data Driven and Digital Medicine (D3M), The Department of Medicine, Icahn School of Medicine at Mount Siniai, New York, NY, USA
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Wu B, Chen J, Zhang X, Feng N, Xiang Z, Wei Y, Xie J, Zhang W. Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study. Medicine (Baltimore) 2022; 101:e32217. [PMID: 36626448 PMCID: PMC9750683 DOI: 10.1097/md.0000000000032217] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
The prognosis of metastatic lung adenocarcinoma (MLUAD) varies greatly. At present, no studies have constructed a satisfactory prognostic model for MLUAD. We identified 44,878 patients with MLUAD. The patients were randomized into the training and validation cohorts. Cox regression models were performed to identify independent prognostic factors. Then, R software was employed to construct a new nomogram for predicting overall survival (OS) of patients with MLUAD. Accuracy was assessed by the concordance index (C-index), receiver operating characteristic curves and calibration plots. Finally, clinical practicability was examined via decision curve analysis. The OS time range for the included populations was 0 to 107 months, and the median OS was 7.00 months. Nineteen variables were significantly associated with the prognosis, and the top 5 prognostic factors were chemotherapy, grade, age, race and surgery. The nomogram has excellent predictive accuracy and clinical applicability compared to the TNM system (C-index: 0.723 vs 0.534). The C-index values were 0.723 (95% confidence interval: 0.719-0.726) and 0.723 (95% confidence interval: 0.718-0.729) in the training and validation cohorts, respectively. The area under the curve for 6-, 12-, and 18-month OS was 0.799, 0.764, and 0.750, respectively, in the training cohort and 0.799, 0.762, and 0.746, respectively, in the validation cohort. The calibration plots show good accuracy, and the decision curve analysis values indicate good clinical applicability and effectiveness. The nomogram model constructed with the above 19 prognostic factors is suitable for predicting the OS of MLUAD and has good predictive accuracy and clinical applicability.
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Affiliation(s)
- Bo Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Jianhui Chen
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xiang Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Nan Feng
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhongtian Xiang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yiping Wei
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Junping Xie
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wenxiong Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China
- * Correspondence: Wenxiong Zhang, Department of Thoracic Surgery, The Second Affiliated Hospital of Nanchang University, 1 Minde Road, Nanchang 330006, China (e-mail: )
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22
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Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Front Cardiovasc Med 2022; 9:945726. [PMID: 36312266 PMCID: PMC9608631 DOI: 10.3389/fcvm.2022.945726] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/09/2022] [Indexed: 11/17/2022] Open
Abstract
Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.
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Affiliation(s)
- Loucia Karatzia
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Nay Aung
- Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,National Institute for Health and Care Research (NIHR) Barts Biomedical Research Centre, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Dunja Aksentijevic
- Centre for Biochemical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom,*Correspondence: Dunja Aksentijevic,
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23
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Siva Kumar S, Al-Kindi S, Tashtish N, Rajagopalan V, Fu P, Rajagopalan S, Madabhushi A. Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring. Front Cardiovasc Med 2022; 9:976769. [PMID: 36277775 PMCID: PMC9580025 DOI: 10.3389/fcvm.2022.976769] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/05/2022] [Indexed: 11/13/2022] Open
Abstract
Background Precision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD. Methods We analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (Str) and internal validation (Sv) sets [Str (1): Sv (1): 50:50; Str (2): Sv (2): 60:40; Str (3): Sv (3): 70:30; Str (4): Sv (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from Str to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of Mecg, Mcac, Mecg+cac and Mnom in predicting CV disease-free survival in ASCVD. Findings Over a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (Str). The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all Sv). The Mecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that Mecg+cac was superior to Mecg (p = 1.8e-10) or Mcac (p < 2.2e-16) alone. The Mnom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, Mnom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables. Conclusion The use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.
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Affiliation(s)
- Shruti Siva Kumar
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States,*Correspondence: Shruti Siva Kumar
| | - Sadeer Al-Kindi
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, United States,School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Nour Tashtish
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, United States,School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Varun Rajagopalan
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, United States,School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Sanjay Rajagopalan
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, United States,School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Anant Madabhushi
- Wallace H. Coulter Department of Biomedical Engineering, Radiology and Imaging Sciences, Biomedical Informatics (BMI) and Pathology, Georgia Institute of Technology and Emory University, Research Health Scientist, Atlanta Veterans Administration Medical Center, Atlanta, GA, United States
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24
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Predicting Overall Survival in Patients with Nonmetastatic Gastric Signet Ring Cell Carcinoma: A Machine Learning Approach. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4862376. [PMID: 36148015 PMCID: PMC9489421 DOI: 10.1155/2022/4862376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/16/2022] [Accepted: 08/24/2022] [Indexed: 11/30/2022]
Abstract
Background and Aims Accurate prediction is essential for the survival of patients with nonmetastatic gastric signet ring cell carcinoma (GSRC) and medical decision-making. Current models rely on prespecified variables, limiting their performance and not being suitable for individual patients. Our study is aimed at developing a more precise model for predicting 1-, 3-, and 5-year overall survival (OS) in patients with nonmetastatic GSRC based on a machine learning approach. Methods We selected 2127 GSRC patients diagnosed from 2004 to 2014 from the Surveillance, Epidemiology, and End Results (SEER) database and then randomly partitioned them into a training and validation cohort. We compared the performance of several machine learning-based models and finally chose the eXtreme gradient boosting (XGBoost) model as the optimal method to predict the OS in patients with nonmetastatic GSRC. The model was assessed using the receiver operating characteristic curve (ROC). Results In the training cohort, for predicting OS rates at 1-, 3-, and 5-year, the AUCs of the XGBoost model were 0.842, 0.831, and 0.838, respectively, while in the testing cohort, the AUCs of 1-, 3-, and 5-year OS rates were 0.749, 0.823, and 0.829, respectively. Besides, the XGBoost model also performed better when compared with the American Joint Committee on Cancer (AJCC) stage. The performance for this model was stably maintained when stratified by age and ethnicity. Conclusion The XGBoost-based model accurately predicts the 1-, 3-, and 5-year OS in patients with nonmetastatic GSRC. Machine learning is a promising way to predict the survival outcomes of tumor patients.
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25
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Kuznetsova N, Gubina A, Sagirova Z, Dhif I, Gognieva D, Melnichuk A, Orlov O, Syrkina E, Sedov V, Chomakhidze P, Saner H, Kopylov P. Left Ventricular Diastolic Dysfunction Screening by a Smartphone-Case Based on Single Lead ECG. CLINICAL MEDICINE INSIGHTS-CARDIOLOGY 2022; 16:11795468221120088. [PMID: 36046179 PMCID: PMC9421020 DOI: 10.1177/11795468221120088] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/26/2022] [Indexed: 12/03/2022]
Abstract
Aims To investigate the potential of a signal processed by smartphone-case based on single lead electrocardiogram (ECG) for left ventricular diastolic dysfunction (LVDD) determination as a screening method. Methods and Results We included 446 subjects for sample learning and 259 patients for sample test aged 39 to 74 years for testing with 2D-echocardiography, tissue Doppler imaging and ECG using a smartphone-case based single lead ECG monitor for the assessment of LVDD. Spectral analysis of ECG signals (spECG) has been used in combination with advanced signal processing and artificial intelligence methods. Wavelengths slope, time intervals between waves, amplitudes at different points of the ECG complexes, energy of the ECG signal and asymmetry indices were analyzed. The QTc interval indicated significant diastolic dysfunction with a sensitivity of 78% and a specificity of 65%, a Tpeak parameter >590 ms with 63% and 58%, a T value off >695 ms with 63% and 74%, and QRSfi > 674 ms with 74% and 57%, respectively. A combination of the threshold values from all 4 parameters increased sensitivity to 86% and specificity to 70%, respectively (OR 11.7 [2.7-50.9], P < .001). Algorithm approbation have shown: Sensitivity-95.6%, Specificity-97.7%, Diagnostic accuracy-96.5% and Repeatability-98.8%. Conclusion Our results indicate a great potential of a smartphone-case based on single lead ECG as novel screening tool for LVDD if spECG is used in combination with advanced signal processing and machine learning technologies.
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Affiliation(s)
- Natalia Kuznetsova
- World-Class Research Center “Digital Biodesign and Personalized Healthcare” Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anastasiia Gubina
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Zhanna Sagirova
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Ines Dhif
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Daria Gognieva
- World-Class Research Center “Digital Biodesign and Personalized Healthcare” Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anna Melnichuk
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Oleg Orlov
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Elena Syrkina
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vsevolod Sedov
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Petr Chomakhidze
- World-Class Research Center “Digital Biodesign and Personalized Healthcare” Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
| | - Hugo Saner
- World-Class Research Center “Digital Biodesign and Personalized Healthcare” Sechenov First Moscow State Medical University, Moscow, Russia
- ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Philippe Kopylov
- World-Class Research Center “Digital Biodesign and Personalized Healthcare” Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia
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Mehta C, Shah R, Yanamala N, Sengupta PP. Cardiovascular Imaging Databases: Building Machine Learning Algorithms for Regenerative Medicine. CURRENT STEM CELL REPORTS 2022. [DOI: 10.1007/s40778-022-00216-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Ulloa-Cerna AE, Jing L, Pfeifer JM, Raghunath S, Ruhl JA, Rocha DB, Leader JB, Zimmerman N, Lee G, Steinhubl SR, Good CW, Haggerty CM, Fornwalt BK, Chen R. rECHOmmend: An ECG-based Machine-learning Approach for Identifying Patients at High-risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography. Circulation 2022; 146:36-47. [PMID: 35533093 PMCID: PMC9241668 DOI: 10.1161/circulationaha.121.057869] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Background: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We developed a novel ECG-based machine learning approach to predict multiple structural heart conditions, hypothesizing that a composite model would yield higher prevalence and positive predictive values to facilitate meaningful recommendations for echocardiography. Methods: Using 2 232 130 ECGs linked to electronic health records and echocardiography reports from 484 765 adults between 1984 to 2021, we trained machine learning models to predict the presence or absence of any of 7 echocardiography-confirmed diseases within 1 year. This composite label included the following: moderate or severe valvular disease (aortic/mitral stenosis or regurgitation, tricuspid regurgitation), reduced ejection fraction <50%, or interventricular septal thickness >15 mm. We tested various combinations of input features (demographics, laboratory values, structured ECG data, ECG traces) and evaluated model performance using 5-fold cross-validation, multisite validation trained on 1 site and tested on 10 independent sites, and simulated retrospective deployment trained on pre-2010 data and deployed in 2010. Results: Our composite rECHOmmend model used age, sex, and ECG traces and had a 0.91 area under the receiver operating characteristic curve and a 42% positive predictive value at 90% sensitivity, with a composite label prevalence of 17.9%. Individual disease models had area under the receiver operating characteristic curves from 0.86 to 0.93 and lower positive predictive values from 1% to 31%. Area under the receiver operating characteristic curves for models using different input features ranged from 0.80 to 0.93, increasing with additional features. Multisite validation showed similar results to cross-validation, with an aggregate area under the receiver operating characteristic curve of 0.91 across our independent test set of 10 clinical sites after training on a separate site. Our simulated retrospective deployment showed that for ECGs acquired in patients without preexisting structural heart disease in the year 2010, 11% were classified as high risk and 41% (4.5% of total patients) developed true echocardiography-confirmed disease within 1 year. Conclusions: An ECG-based machine learning model using a composite end point can identify a high-risk population for having undiagnosed, clinically significant structural heart disease while outperforming single-disease models and improving practical utility with higher positive predictive values. This approach can facilitate targeted screening with echocardiography to improve underdiagnosis of structural heart disease.
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Affiliation(s)
- Alvaro E Ulloa-Cerna
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA
| | - Linyuan Jing
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA
| | - John M Pfeifer
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA; Heart and Vascular Center, Evangelical Hospital, Lewisburg, PA; Tempus Labs Inc, Chicago, IL
| | - Sushravya Raghunath
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA; Tempus Labs Inc, Chicago, IL
| | - Jeffrey A Ruhl
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA
| | - Daniel B Rocha
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA
| | - Joseph B Leader
- Phenomic Analytics and Clinical Data Core, Geisinger, Danville, PA; Tempus Labs Inc, Chicago, IL
| | | | | | - Steven R Steinhubl
- Tempus Labs Inc, Chicago, IL; Scripps Research Translational Institute, La Jolla, CA
| | - Christopher W Good
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA; UPMC Heart and Vascular Institute at UPMC, Hamot, PA
| | - Christopher M Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA; Heart Institute, Geisinger, Danville, PA
| | - Brandon K Fornwalt
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA; Tempus Labs Inc, Chicago, IL; Heart Institute, Geisinger, Danville, PA; Department of Radiology, Geisinger, Danville, PA
| | - RuiJun Chen
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA; Tempus Labs Inc, Chicago, IL; Department of Medicine, Geisinger, Danville, PA
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28
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Singh A, Sun D, Mor-Avi V, Addetia K, Patel AR, DeCara JM, Ward RP, Lang RM. Can echocardiographic assessment of diastolic function be automated? THE INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING 2022; 38:965-974. [PMID: 34882301 DOI: 10.1007/s10554-021-02488-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/03/2021] [Indexed: 10/19/2022]
Abstract
Echocardiographic evaluation of left ventricular diastolic function relies on a multi-pronged algorithm, which incorporates Doppler-based and volumetric parameters. Integration of clinical data in diastolic assessment is recommended, though not clearly outlined. We sought to develop an automated tool for diastolic function, compare its performance to human-generated diagnoses and identify the common sources of error. Our software tool is based on the 2016 diastolic guidelines algorithm, which uses 8 parameters as input, with 10 conditions as the logic and 5 possible outputs as final diagnoses. Initially, we prospectively studied 563 patients whose diastolic function was independently evaluated by an expert echocardiographer and by the automated tool. Incongruent cases were further analyzed, after which features of myocardial disease were integrated into a refined version of the software that was tested in an independent cohort of 1106 patients. In the initial analysis, 202/563 grades (36%) were incongruent between the automated and human reads, with the highest rate of discordance for mild and indeterminate categories. In 17% of cases, human diagnoses differed from that dictated by the algorithm due to integration of clinical factors. Follow-up analysis using the refined automated tool did not improve the discordance rate (440/1106; 40%). There was more discordance in cases of: age > 40 years, impaired mitral inflow patterns (E/A < 0.8) and reduced mitral e' values. Further analysis revealed differences in how readers interpreted the interaction between these factors and diastolic function, which could not be incorporated into the automated tool. In conclusion, although assessment of diastolic function relies on an algorithm that can be automated, this algorithm does not include clear guidance on how to incorporate age, or age-related changes in Doppler-based parameters, often resulting in discordant diagnoses. Standardized interpretation of these factors is needed to improve the reproducibility of diastolic function grading by human readers and the accuracy of the automated classification.
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Affiliation(s)
- Amita Singh
- Department of Medicine, University of Chicago Medical Center, 5758 S. Maryland Ave., MC 9067, DCAM 5512, Chicago, IL, 60637, USA.
| | - Deyu Sun
- Philips Healthcare, Cambridge, MA, USA
| | - Victor Mor-Avi
- Department of Medicine, University of Chicago Medical Center, 5758 S. Maryland Ave., MC 9067, DCAM 5512, Chicago, IL, 60637, USA
| | - Karima Addetia
- Department of Medicine, University of Chicago Medical Center, 5758 S. Maryland Ave., MC 9067, DCAM 5512, Chicago, IL, 60637, USA
| | - Amit R Patel
- Department of Medicine, University of Chicago Medical Center, 5758 S. Maryland Ave., MC 9067, DCAM 5512, Chicago, IL, 60637, USA
| | - Jeanne M DeCara
- Department of Medicine, University of Chicago Medical Center, 5758 S. Maryland Ave., MC 9067, DCAM 5512, Chicago, IL, 60637, USA
| | - R Parker Ward
- Department of Medicine, University of Chicago Medical Center, 5758 S. Maryland Ave., MC 9067, DCAM 5512, Chicago, IL, 60637, USA
| | - Roberto M Lang
- Department of Medicine, University of Chicago Medical Center, 5758 S. Maryland Ave., MC 9067, DCAM 5512, Chicago, IL, 60637, USA
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Lee CH, Liu WT, Lou YS, Lin CS, Fang WH, Lee CC, Ho CL, Wang CH, Lin C. Artificial intelligence-enabled electrocardiogram screens low left ventricular ejection fraction with a degree of confidence. Digit Health 2022; 8:20552076221143249. [PMID: 36532114 PMCID: PMC9751170 DOI: 10.1177/20552076221143249] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 11/17/2022] [Indexed: 09/10/2024] Open
Abstract
Background Artificial intelligence-enabled electrocardiogram has become a substitute tool for echocardiography in left ventricular ejection fraction estimation. However, the direct use of artificial intelligence-enabled electrocardiogram may be not trustable due to the uncertainty of the prediction. Objective The study aimed to establish an artificial intelligence-enabled electrocardiogram with a degree of confidence to identify left ventricular dysfunction. Methods The study collected 76,081 and 11,771 electrocardiograms from an academic medical center and a community hospital to establish and validate the deep learning model, respectively. The proposed deep learning model provided the point estimation of the actual ejection fraction and its standard deviation derived from the maximum probability density function of a normal distribution. The primary analysis focused on the accuracy of identifying patients with left ventricular dysfunction (ejection fraction ≤ 40%). Since the standard deviation was an uncertainty indicator in a normal distribution, we used it as a degree of confidence in the artificial intelligence-enabled electrocardiogram. We further explored the clinical application of estimated standard deviation and followed up on the new-onset left ventricular dysfunction in patients with initially normal ejection fraction. Results The area under receiver operating characteristic curves (AUC) of detecting left ventricular dysfunction were 0.9549 and 0.9365 in internal and external validation sets. After excluding the cases with a lower degree of confidence, the artificial intelligence-enabled electrocardiogram performed better in the remaining cases in internal (AUC = 0.9759) and external (AUC = 0.9653) validation sets. For the application of future left ventricular dysfunction risk stratification in patients with initially normal ejection fraction, a 4.57-fold risk of future left ventricular dysfunction when the artificial intelligence-enabled electrocardiogram is positive in the internal validation set. The hazard ratio was increased to 8.67 after excluding the cases with a lower degree of confidence. This trend was also validated in the external validation set. Conclusion The deep learning model with a degree of confidence can provide advanced improvements in identifying left ventricular dysfunction and serve as a decision support and management-guided screening tool for prognosis.
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Affiliation(s)
- Chun-Ho Lee
- School of Public Health, National Defense Medical Center, Taipei
| | - Wei-Ting Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei
| | - Yu-Sheng Lou
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei
| | - Wen-Hui Fang
- Department of Family and Community Medicine, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei
| | - Ching-Liang Ho
- Division of Hematology and Oncology, Tri-Service General Hospital, National Defense Medical Center, Taipei
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei
| | - Chin Lin
- School of Public Health, National Defense Medical Center, Taipei
- Graduate Institutes of Life Sciences, National Defense Medical Center, Taipei
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei
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Machine Learning to Calculate Heparin Dose in COVID-19 Patients with Active Cancer. J Clin Med 2021; 11:jcm11010219. [PMID: 35011959 PMCID: PMC8746167 DOI: 10.3390/jcm11010219] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 12/22/2021] [Accepted: 12/24/2021] [Indexed: 12/21/2022] Open
Abstract
To realize a machine learning (ML) model to estimate the dose of low molecular weight heparin to be administered, preventing thromboembolism events in COVID-19 patients with active cancer. Methods: We used a dataset comprising 131 patients with active cancer and COVID-19. We considered five ML models: logistic regression, decision tree, random forest, support vector machine and Gaussian naive Bayes. We decided to implement the logistic regression model for our study. A model with 19 variables was analyzed. Data were randomly split into training (70%) and testing (30%) sets. Model performance was assessed by confusion matrix metrics on the testing data for each model as positive predictive value, sensitivity and F1-score. Results: We showed that the five selected models outperformed classical statistical methods of predictive validity and logistic regression was the most effective, being able to classify with an accuracy of 81%. The most relevant result was finding a patient-proof where python function was able to obtain the exact dose of low weight molecular heparin to be administered and thereby to prevent the occurrence of VTE. Conclusions: The world of machine learning and artificial intelligence is constantly developing. The identification of a specific LMWH dose for preventing VTE in very high-risk populations, such as the COVID-19 and active cancer population, might improve with the use of new training ML-based algorithms. Larger studies are needed to confirm our exploratory results.
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Izumi C. Evaluation of Diastolic Dysfunction in Heart Failure With Preserved Ejection Fraction (HFpEF) - Is It Possible to Delineate the Phenotype of HFpEF? Circ J 2021; 86:34-36. [PMID: 34497160 DOI: 10.1253/circj.cj-21-0658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Chisato Izumi
- Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center
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32
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Affiliation(s)
- Tomoko Ishizu
- Department of Cardiology, Faculty of Medicine, University of Tsukuba
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33
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Van Ommen AM, Kessler EL, Valstar G, Onland-Moret NC, Cramer MJ, Rutten F, Coronel R, Den Ruijter H. Electrocardiographic Features of Left Ventricular Diastolic Dysfunction and Heart Failure With Preserved Ejection Fraction: A Systematic Review. Front Cardiovasc Med 2021; 8:772803. [PMID: 34977187 PMCID: PMC8719440 DOI: 10.3389/fcvm.2021.772803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/16/2021] [Indexed: 01/08/2023] Open
Abstract
Background: Electrocardiographic features are well-known for heart failure with reduced ejection fraction (HFrEF), but not for left ventricular diastolic dysfunction (LVDD) and heart failure with preserved ejection fraction (HFpEF). As ECG features could help to identify high-risk individuals in primary care, we systematically reviewed the literature for ECG features diagnosing women and men suspected of LVDD and HFpEF. Methods and Results: Among the 7,127 records identified, only 10 studies reported diagnostic measures, of which 9 studied LVDD. For LVDD, the most promising features were T-end-P/(PQ*age), which is the electrocardiographic equivalent of the passive-to-active filling (AUC: 0.91-0.96), and repolarization times (QTc interval ≥ 350 ms, AUC: 0.85). For HFpEF, the Cornell product ≥ 1,800 mm*ms showed poor sensitivity of 40% (AUC: 0.62). No studies presented results stratified by sex. Conclusion: Electrocardiographic features are not widely evaluated in diagnostic studies for LVDD and HFpEF. Only for LVDD, two ECG features related to the diastolic interval, and repolarization measures showed diagnostic potential. To improve diagnosis and care for women and men suspected of heart failure, reporting of sex-specific data on ECG features is encouraged.
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Affiliation(s)
- Anne-Mar Van Ommen
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Elise Laura Kessler
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Gideon Valstar
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - N. Charlotte Onland-Moret
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Maarten Jan Cramer
- Department of Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Frans Rutten
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Ruben Coronel
- Department of Experimental Cardiology, Amsterdam University Medical Center, Amsterdam, Netherlands
- Institut de rythmologie et modélisation cardiaque (IHU-Liryc), Pessac, France
| | - Hester Den Ruijter
- Laboratory of Experimental Cardiology, University Medical Center Utrecht, Utrecht, Netherlands
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Unterhuber M, Rommel KP, Kresoja KP, Lurz J, Kornej J, Hindricks G, Scholz M, Thiele H, Lurz P. Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:699-703. [PMID: 36713109 PMCID: PMC9707942 DOI: 10.1093/ehjdh/ztab081] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 08/23/2021] [Accepted: 09/14/2021] [Indexed: 02/01/2023]
Abstract
Aims Heart failure with preserved ejection fraction (HFpEF) is a rapidly growing global health problem. To date, diagnosis of HFpEF is based on clinical, invasive, and laboratory examinations. Electrocardiographic findings may vary, and there are no known typical ECG features for HFpEF. Methods and results This study included two patient cohorts. In the derivation cohort, we included n = 1884 patients who presented with exertional dyspnoea or equivalent and preserved ejection fraction (≥50%) and clinical suspicion for coronary artery disease. The ECGs were divided in segments, yielding a total of 77 558 samples. We trained a convolutional neural network (CNN) to classify HFpEF and control patients according to European Society of Cardiology (ESC) criteria. An external group of 203 volunteers in a prospective heart failure screening programme served as a validation cohort of the CNN. The external validation of the CNN yielded an area under the curve of 0.80 [95% confidence interval (CI) 0.74-0.86] for detection of HFpEF according to ESC criteria, with a sensitivity of 0.99 (95% CI 0.98-0.99) and a specificity of 0.60 (95% CI 0.56-0.64), with a positive predictive value of 0.68 (95%CI 0.64-0.72) and a negative predictive value of 0.98 (95% CI 0.95-0.99). Conclusion In this study, we report the first deep learning-enabled CNN for identifying patients with HFpEF according to ESC criteria including NT-proBNP measurements in the diagnostic algorithm among patients at risk. The suitability of the CNN was validated on an external validation cohort of patients at risk for developing heart failure, showing a convincing screening performance.
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Affiliation(s)
- Matthias Unterhuber
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Strümpellstraße 39, 04289 Leipzig, Germany
| | - Karl-Philipp Rommel
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Strümpellstraße 39, 04289 Leipzig, Germany
| | - Karl-Patrik Kresoja
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Strümpellstraße 39, 04289 Leipzig, Germany
| | - Julia Lurz
- Department of Electrophysiology, Heart Center Leipzig at University Leipzig, Strümpellstraße 39, 04289 Leipzig, Germany
| | - Jelena Kornej
- School of Medicine, Cardiovascular Medicine, Boston University School of Medicine, 72 E Concord St, Boston, MA 02118
- LIFE Research Center of Civilization Diseases, Philipp-Rosenthal-Straße, 27 04103 Leipzig, Germany
| | - Gerhard Hindricks
- Department of Electrophysiology, Heart Center Leipzig at University Leipzig, Strümpellstraße 39, 04289 Leipzig, Germany
- Leipzig Heart Institute, Russenstraße 69A, 04289 Leipzig, Germany
| | - Markus Scholz
- LIFE Research Center of Civilization Diseases, Philipp-Rosenthal-Straße, 27 04103 Leipzig, Germany
- Institute for Medical Informatics, Statistics and Epidemiology, Medical Faculty, Universität Leipzig Härtelstraße, 16-18 04107 Leipzig, Germany
| | - Holger Thiele
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Strümpellstraße 39, 04289 Leipzig, Germany
- Leipzig Heart Institute, Russenstraße 69A, 04289 Leipzig, Germany
| | - Philipp Lurz
- Department of Cardiology, Heart Center Leipzig at University Leipzig, Strümpellstraße 39, 04289 Leipzig, Germany
- Leipzig Heart Institute, Russenstraße 69A, 04289 Leipzig, Germany
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Vaid A, Johnson KW, Badgeley MA, Somani SS, Bicak M, Landi I, Russak A, Zhao S, Levin MA, Freeman RS, Charney AW, Kukar A, Kim B, Danilov T, Lerakis S, Argulian E, Narula J, Nadkarni GN, Glicksberg BS. Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram. JACC Cardiovasc Imaging 2021; 15:395-410. [PMID: 34656465 PMCID: PMC8917975 DOI: 10.1016/j.jcmg.2021.08.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/29/2021] [Accepted: 08/05/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVES This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population. BACKGROUND Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right- ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only. METHODS A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation. RESULTS We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation. CONCLUSIONS DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.
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Affiliation(s)
- Akhil Vaid
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kipp W Johnson
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Sulaiman S Somani
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mesude Bicak
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Isotta Landi
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adam Russak
- Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Shan Zhao
- The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matthew A Levin
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Robert S Freeman
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Alexander W Charney
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Atul Kukar
- Department of Cardiology, Mount Sinai Queens Hospital, Astoria, New York, USA, and Icahn School of Medicine at Mount Sinai, New York, New York, USA; Division of Cardiology, Mount Sinai West Hospital and Icahn School of Medicine at Mount Sinai, New York, New York USA
| | - Bette Kim
- Mount Sinai Beth Israel Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Tatyana Danilov
- Department of Cardiology, Mount Sinai Morningside Hospital, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Stamatios Lerakis
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Benjamin S Glicksberg
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA; The Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
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Zaman F, Ponnapureddy R, Wang YG, Chang A, Cadaret LM, Abdelhamid A, Roy SD, Makan M, Zhou R, Jayanna MB, Gnall E, Dai X, Singh A, Zheng J, Boppana VS, Wang F, Singh P, Wu X, Liu K. Spatio-temporal hybrid neural networks reduce erroneous human "judgement calls" in the diagnosis of Takotsubo syndrome. EClinicalMedicine 2021; 40:101115. [PMID: 34522872 PMCID: PMC8426197 DOI: 10.1016/j.eclinm.2021.101115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 08/08/2021] [Accepted: 08/16/2021] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND We investigate whether deep learning (DL) neural networks can reduce erroneous human "judgment calls" on bedside echocardiograms and help distinguish Takotsubo syndrome (TTS) from anterior wall ST segment elevation myocardial infarction (STEMI). METHODS We developed a single-channel (DCNN[2D SCI]), a multi-channel (DCNN[2D MCI]), and a 3-dimensional (DCNN[2D+t]) deep convolution neural network, and a recurrent neural network (RNN) based on 17,280 still-frame images and 540 videos from 2-dimensional echocardiograms in 10 years (1 January 2008 to 1 January 2018) retrospective cohort in University of Iowa (UI) and eight other medical centers. Echocardiograms from 450 UI patients were randomly divided into training and testing sets for internal training, testing, and model construction. Echocardiograms of 90 patients from the other medical centers were used for external validation to evaluate the model generalizability. A total of 49 board-certified human readers performed human-side classification on the same echocardiography dataset to compare the diagnostic performance and help data visualization. FINDINGS The DCNN (2D SCI), DCNN (2D MCI), DCNN(2D+t), and RNN models established based on UI dataset for TTS versus STEMI prediction showed mean diagnostic accuracy 73%, 75%, 80%, and 75% respectively, and mean diagnostic accuracy of 74%, 74%, 77%, and 73%, respectively, on the external validation. DCNN(2D+t) (area under the curve [AUC] 0·787 vs. 0·699, P = 0·015) and RNN models (AUC 0·774 vs. 0·699, P = 0·033) outperformed human readers in differentiating TTS and STEMI by reducing human erroneous judgement calls on TTS. INTERPRETATION Spatio-temporal hybrid DL neural networks reduce erroneous human "judgement calls" in distinguishing TTS from anterior wall STEMI based on bedside echocardiographic videos. FUNDING University of Iowa Obermann Center for Advanced Studies Interdisciplinary Research Grant, and Institute for Clinical and Translational Science Grant. National Institutes of Health Award (1R01EB025018-01).
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Affiliation(s)
- Fahim Zaman
- Department of Electrical and Electronic Engineering, University of Iowa, Iowa city, IA, United States
| | - Rakesh Ponnapureddy
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, IA, United States
| | - Yi Grace Wang
- Department of Mathematics, California State University Dominguez Hills, Carson, CA, United States
| | - Amanda Chang
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, IA, United States
| | - Linda M Cadaret
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, IA, United States
| | - Ahmed Abdelhamid
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, IA, United States
| | - Shubha D Roy
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, IA, United States
| | - Majesh Makan
- Division of Cardiology, Department of Medicine, Washington University in St. Louis, St. Louis, MO, United States
| | - Ruihai Zhou
- Division of Cardiology, Department of Medicine, University of North Carolina, Chapel Hill, United States
| | - Manju B Jayanna
- Division of Cardiology, Department of Medicine, Lankenau Medical Center, Wynnewood, PA, United States
| | - Eric Gnall
- Division of Cardiology, Department of Medicine, Lankenau Medical Center, Wynnewood, PA, United States
| | - Xuming Dai
- Department of Cardiology, New York Presbyterian Queens/Weill Cornell Medical College, New York City, NY, United States
| | - Avneet Singh
- Division of Cardiology, Department of Medicine, State University of New York, Syracuse, NY, United States
| | - Jingsheng Zheng
- Department of Cardiology, AtlaniCare Regional Medical Center, Pomona, NJ, United States
| | - Venkata S Boppana
- Division of Cardiology, Department of Medicine, University of Kansas-Wichita, Wichita, KS, United States
| | - Feng Wang
- Department of Cardiology, Providence Regional Medical Center, Washington State University, Everett, WA, United States
| | - Pahul Singh
- Department of Cardiology, Northwest Health Medical Center, Bentonville, AR, United States
| | - Xiaodong Wu
- Department of Electrical and Electronic Engineering, University of Iowa, Iowa city, IA, United States
- Corresponding authors.
| | - Kan Liu
- Division of Cardiology, Department of Medicine, University of Iowa, Iowa City, IA, United States
- Corresponding authors.
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Abstract
This article traces the development of automated electrocardiography from its beginnings in Washington, DC around 1960 through to its current widespread application worldwide. Changes in the methodology of recording ECGs in analogue form using sizeable equipment through to digital recording, even in wearables, are included. Methods of analysis are considered from single lead to three leads to twelve leads. Some of the influential figures are mentioned while work undertaken locally is used to outline the progress of the technique mirrored in other centres. Applications of artificial intelligence are also considered so that the reader can find out how the field has been constantly evolving over the past 50 years.
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van de Bovenkamp AA, Enait V, de Man FS, Oosterveer FTP, Bogaard HJ, Vonk Noordegraaf A, van Rossum AC, Handoko ML. Validation of the 2016 ASE/EACVI Guideline for Diastolic Dysfunction in Patients With Unexplained Dyspnea and a Preserved Left Ventricular Ejection Fraction. J Am Heart Assoc 2021; 10:e021165. [PMID: 34476984 PMCID: PMC8649534 DOI: 10.1161/jaha.121.021165] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background Echocardiography is considered the cornerstone of the diagnostic workup of heart failure with preserved ejection fraction. Thus far, validation of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) echo‐algorithm for evaluation of diastolic (dys)function in a patient suspected of heart failure with preserved ejection fraction has been limited. Methods and Results The diagnostic performance of the 2016 ASE/EACVI algorithm was assessed in 204 patients evaluated for unexplained dyspnea or pulmonary hypertension with echocardiogram and right heart catheterization. Invasively measured pulmonary capillary wedge pressure (PCWP) was used as the gold standard. In addition, the diagnostic performance of H2FPEF score and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) were evaluated. There was a poor correlation between indexed left atrial volume, E/e′ (septal and average) or early mitral inflow (E), and PCWP (r=0.25–0.30, P values all <0.01). No correlation was found in our cohort between e′ (septal or lateral) or tricuspid valve regurgitation and PCWP. The correlation between diastolic function grades of the ASE/EACVI algorithm and PCWP was poor (r=0.17, P<0.05). The ASE/EACVI algorithm had a sensitivity and specificity of 35% and 87%, respectively; an accuracy of 67% and an area under the curve of 0.56. Moreover, in 30% of cases the algorithm was not applicable or indeterminate. H2FPEF score had a modest correlation with PCWP (r=0.44, P<0.0001), and accuracy was 73%; NT‐proBNP correlated weakly with PCWP (r=0.24, P<0.001), and accuracy was 57%. Conclusions The 2016 ASE/EACVI algorithm for the assessment of diastolic function has a limited diagnostic accuracy in patients evaluated for unexplained dyspnea and/or pulmonary hypertension, and especially sensitivity to detect diastolic dysfunction was low.
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Affiliation(s)
- Arno A van de Bovenkamp
- Department of Cardiology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Vidya Enait
- Department of Cardiology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Frances S de Man
- Department of Pulmonology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Frank T P Oosterveer
- Department of Pulmonology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Harm Jan Bogaard
- Department of Pulmonology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Anton Vonk Noordegraaf
- Department of Pulmonology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - Albert C van Rossum
- Department of Cardiology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
| | - M Louis Handoko
- Department of Cardiology Amsterdam Cardiovascular Sciences (ACS) Amsterdam UMC, Vrije Universiteit Amsterdam Amsterdam The Netherlands
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Al Hinai G, Jammoul S, Vajihi Z, Afilalo J. Deep learning analysis of resting electrocardiograms for the detection of myocardial dysfunction, hypertrophy, and ischaemia: a systematic review. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:416-423. [PMID: 34604757 PMCID: PMC8482047 DOI: 10.1093/ehjdh/ztab048] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/14/2021] [Indexed: 01/31/2023]
Abstract
The aim of this review was to assess the evidence for deep learning (DL) analysis of resting electrocardiograms (ECGs) to predict structural cardiac pathologies such as left ventricular (LV) systolic dysfunction, myocardial hypertrophy, and ischaemic heart disease. A systematic literature search was conducted to identify published original articles on end-to-end DL analysis of resting ECG signals for the detection of structural cardiac pathologies. Studies were excluded if the ECG was acquired by ambulatory, stress, intracardiac, or implantable devices, and if the pathology of interest was arrhythmic in nature. After duplicate reviewers screened search results, 12 articles met the inclusion criteria and were included. Three articles used DL to detect LV systolic dysfunction, achieving an area under the curve (AUC) of 0.89-0.93 and an accuracy of 98%. One study used DL to detect LV hypertrophy, achieving an AUC of 0.87 and an accuracy of 87%. Six articles used DL to detect acute myocardial infarction, achieving an AUC of 0.88-1.00 and an accuracy of 83-99.9%. Two articles used DL to detect stable ischaemic heart disease, achieving an accuracy of 95-99.9%. Deep learning models, particularly those that used convolutional neural networks, outperformed rules-based models and other machine learning models. Deep learning is a promising technique to analyse resting ECG signals for the detection of structural cardiac pathologies, which has clinical applicability for more effective screening of asymptomatic populations and expedited diagnostic work-up of symptomatic patients at risk for cardiovascular disease.
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Affiliation(s)
- Ghalib Al Hinai
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada
| | - Samer Jammoul
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada
| | - Zara Vajihi
- Department of Emergency Medicine, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, H-126, Montreal, QC H3T 1E2, Canada
| | - Jonathan Afilalo
- Division of Cardiology, Jewish General Hospital, McGill University, 3755 Cote Ste Catherine Rd, E-222, Montreal, QC H3T 1E2, Canada
- Centre for Clinical Epidemiology, Jewish General Hospital, 3755 Cote Ste Catherine Rd, H-411, Montreal, QC H3T 1E2, Canada
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Cai A, Zhu Y, Clarkson SA, Feng Y. The Use of Machine Learning for the Care of Hypertension and Heart Failure. JACC. ASIA 2021; 1:162-172. [PMID: 36338169 PMCID: PMC9627876 DOI: 10.1016/j.jacasi.2021.07.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 06/22/2021] [Accepted: 07/19/2021] [Indexed: 06/12/2023]
Abstract
Machine learning (ML) is a branch of artificial intelligence that combines computer science, statistics, and decision theory to learn complex patterns from voluminous data. In the last decade, accumulating evidence has shown the utility of ML for prediction, diagnosis, and classification of hypertension and heart failure (HF). In addition, ML-enabled image analysis has potential value in assessing cardiac structure and function in an accurate, scalable, and efficient way. Considering the high burden of hypertension and HF in China and worldwide, ML may help address these challenges from different aspects. Indeed, prior studies have shown that ML can enhance each stage of patient care, from research and development, to daily clinical practice and population health. Through reviewing the published literature, the aims of the current systemic review are to summarize the utilities of ML for the care of those with hypertension and HF.
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Key Words
- ANN, artificial neural network
- AUC, area under the curve
- CNN, convolutional neural network
- HFpEF, heart failure with preserved ejection fraction
- LRM, linear or logistic regression model
- LVDD, left ventricular diastolic dysfunction
- LVH, left ventricular hypertrophy
- ML, machine learning
- RF, random forest
- SVM, support vector machine
- algorithms
- heart failure
- hypertension machine learning
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Affiliation(s)
- Anping Cai
- Department of Cardiology, Guangdong Cardiovascular Institute, Hypertension Research Laboratory, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Yicheng Zhu
- Department of Cardiology, Guangdong Cardiovascular Institute, Hypertension Research Laboratory, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Stephen A. Clarkson
- Division of Cardiovascular Disease, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Yingqing Feng
- Department of Cardiology, Guangdong Cardiovascular Institute, Hypertension Research Laboratory, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
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Patient Phenotypes and SGLT-2 Inhibition in Type 2 Diabetes: Insights From the EMPA-REG OUTCOME Trial. JACC-HEART FAILURE 2021; 9:568-577. [PMID: 34325887 DOI: 10.1016/j.jchf.2021.03.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 03/15/2021] [Accepted: 03/15/2021] [Indexed: 11/23/2022]
Abstract
OBJECTIVES Using latent class analysis (LCA) of EMPA-REG OUTCOME (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients), this study identified distinct phenotypes in subjects with type 2 diabetes (T2D) and cardiovascular (CV) disease and explored treatment effects across phenotypes. BACKGROUND In the EMPA-REG OUTCOME trial, empagliflozin reduced risk of CV death or hospitalization for heart failure (HHF) by 34% in subjects with T2D and CV disease. Among such subjects, there has been limited evaluation of clinical phenotypes. METHODS Overall, 7,020 participants were treated with empagliflozin 25 mg, 10 mg, or placebo. For this post hoc analysis, participants were randomly separated into training (two-thirds of patients) and validation (remaining one-third) sets. LCA identified 3 phenotype groups (n = 6,639 with complete data). The phenotype association with CV death or HHF and empagliflozin treatment effect across groups was explored by Cox regression (in training and validation sets). RESULTS In the training set, phenotype group 1 (n = 1,463; 33.1%) included younger patients with shorter T2D duration and the highest estimated glomerular filtration rate (eGFR). Phenotype group 2 (n = 1,172; 26.5%) included more women with non-coronary artery disease. Phenotype group 3 (n = 1,785; 40.4%) included older patients with advanced coronary disease and the lowest eGFR. The risk of CV death varied across phenotypes (group 2 vs. 1: hazard ratio [HR]; 1.83; 95% confidence interval [CI]: 1.23 to 2.71; group 3 vs. 1: HR: 1.86; 95% CI: 1.30 to 2.67) with similar patterns for CV death or HHF. Consistent treatment effects of empagliflozin were seen across phenotypes in the training and validation sets (interaction p > 0.30). CONCLUSIONS Among participants with T2D, this study identified 3 phenotypes with varying CV risk. The treatment effect across phenotypes reaffirms the robustness of CV death or HHF reduction with empagliflozin. (BI 10773 [Empagliflozin] Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients [EMPA-REG OUTCOME]; NCT01131676).
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Farjo PD, Sengupta PP. ECG for Screening Cardiac Abnormalities: The Premise and Promise of Machine Learning. Circ Cardiovasc Imaging 2021; 14:e012837. [PMID: 34129345 DOI: 10.1161/circimaging.121.012837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Peter D Farjo
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, WV, USA
| | - Partho P Sengupta
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, Morgantown, WV, USA
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Potter EL, Rodrigues CHM, Ascher DB, Abhayaratna WP, Sengupta PP, Marwick TH. Machine Learning of ECG Waveforms to Improve Selection for Testing for Asymptomatic Left Ventricular Dysfunction Prompt. JACC Cardiovasc Imaging 2021; 14:1904-1915. [PMID: 34147443 DOI: 10.1016/j.jcmg.2021.04.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 03/24/2021] [Accepted: 04/08/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES To identify whether machine learning from processing of continuous wave transforms (CWTs) to provide an "energy waveform" electrocardiogram (ewECG) could be integrated with echocardiographic assessment of subclinical systolic and diastolic left ventricular dysfunction (LVD). BACKGROUND Asymptomatic LVD has management implications, but routine echocardiography is not undertaken in subjects at risk of heart failure. Signal processing of the surface ECG with the use of CWT can identify abnormal myocardial relaxation. METHODS EwECG and echocardiography were undertaken in 398 participants at risk of heart failure (HF). Reduced global longitudinal strain (GLS ≤16%)), diastolic abnormalities (E/e' >15, left atrial enlargement with E/e' >10 or impaired relaxation) or LV hypertrophy defined LVD. EwECG feature selection and supervised machine-learning by random forest (RF) classifier was undertaken with 643 CWT-derived features and the Atherosclerosis Risk in Communities (ARIC) heart failure risk score. RESULTS The ARIC score and 18 CWT features were selected to build a RF predictive model for LVD in a training dataset (n = 287; 60% female, median age 71 [interquartile range: 68 to 74] years). Model performance was tested in an independent group (n = 111; 49% female, median age 61 years [59 to 66 years]), demonstrating 85% sensitivity and 72% specificity (area under the receiver-operating characteristic curve [AUC]: 0.83; 95% confidence interval [CI]: 0.74 to 0.92). With ARIC score removed, sensitivity was 88% and specificity, 70% (AUC: 0.78; 95% CI: 0.70 to 0.86). RF models for reduced GLS and diastolic abnormalities including similar features had sensitivities that were unsuitable for screening. Conventional candidates for LVD screening (ARIC score, N-terminal pro-B-type natriuretic peptide, and standard automated ECG analysis) had inferior discriminative ability. Integration of ewECG in screening of people at risk of HF would reduce need for echocardiography by 45% while missing 12% of LVD cases. CONCLUSIONS Machine learning applied to ewECG is a sensitive screening test for LVD, and its integration into screening of patients at risk for HF would reduce the number of echocardiograms by almost one-half.
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Affiliation(s)
- Elizabeth L Potter
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Carlos H M Rodrigues
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Biomedical Sciences, Melbourne University, Melbourne, Victoria, Australia
| | - David B Ascher
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Biomedical Sciences, Melbourne University, Melbourne, Victoria, Australia
| | - Walter P Abhayaratna
- Australian National University Medical School, Australian National University, Canberra, Australian Capital Territory, Australia; Division of Medicine, Canberra Hospital, Canberra, Australian Capital Territory, Australia
| | - Partho P Sengupta
- West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Thomas H Marwick
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
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Pandey A, Kagiyama N, Yanamala N, Segar MW, Cho JS, Tokodi M, Sengupta PP. Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction. JACC Cardiovasc Imaging 2021; 14:1887-1900. [PMID: 34023263 DOI: 10.1016/j.jcmg.2021.04.010] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/03/2021] [Accepted: 04/01/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct patient subgroups with heart failure with preserved ejection fraction (HFpEF). BACKGROUND The clinical algorithms for phenotyping the severity of diastolic dysfunction in HFpEF remain imprecise. METHODS The authors developed a DeepNN model to predict high- and low-risk phenogroups in a derivation cohort (n = 1,242). Model performance was first validated in 2 external cohorts to identify elevated left ventricular filling pressure (n = 84) and assess its prognostic value (n = 219) in patients with varying degrees of systolic and diastolic dysfunction. In 3 National Heart, Lung, and Blood Institute-funded HFpEF trials, the clinical significance of the model was further validated by assessing the relationships of the phenogroups with adverse clinical outcomes (TOPCAT [Aldosterone Antagonist Therapy for Adults With Heart Failure and Preserved Systolic Function] trial, n = 518), cardiac biomarkers, and exercise parameters (NEAT-HFpEF [Nitrate's Effect on Activity Tolerance in Heart Failure With Preserved Ejection Fraction] and RELAX-HF [Evaluating the Effectiveness of Sildenafil at Improving Health Outcomes and Exercise Ability in People With Diastolic Heart Failure] pooled cohort, n = 346). RESULTS The DeepNN model showed higher area under the receiver-operating characteristic curve than 2016 American Society of Echocardiography guideline grades for predicting elevated left ventricular filling pressure (0.88 vs. 0.67; p = 0.01). The high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization and/or death, even after adjusting for global left ventricular and atrial longitudinal strain (hazard ratio [HR]: 3.96; 95% confidence interval [CI]: 1.24 to 12.67; p = 0.021). Similarly, in the TOPCAT cohort, the high-risk (vs. low-risk) phenogroup showed higher rates of heart failure hospitalization or cardiac death (HR: 1.92; 95% CI: 1.16 to 3.22; p = 0.01) and higher event-free survival with spironolactone therapy (HR: 0.65; 95% CI: 0.46 to 0.90; p = 0.01). In the pooled RELAX-HF/NEAT-HFpEF cohort, the high-risk (vs. low-risk) phenogroup had a higher burden of chronic myocardial injury (p < 0.001), neurohormonal activation (p < 0.001), and lower exercise capacity (p = 0.001). CONCLUSIONS This publicly available DeepNN classifier can characterize the severity of diastolic dysfunction and identify a specific subgroup of patients with HFpEF who have elevated left ventricular filling pressures, biomarkers of myocardial injury and stress, and adverse events and those who are more likely to respond to spironolactone.
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Affiliation(s)
- Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Nobuyuki Kagiyama
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Department of Cardiovascular Biology and Medicine, Juntendo University, Tokyo, Japan; Department of Digital Health and Telemedicine R & D, Juntendo University, Tokyo, Japan
| | - Naveena Yanamala
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA
| | - Matthew W Segar
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Jung S Cho
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Division of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Márton Tokodi
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Heart and Vascular Center, Seemelweis University, Budapest, Hungary
| | - Partho P Sengupta
- Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA.
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Abstract
PURPOSE OF REVIEW Refinement in machine learning (ML) techniques and approaches has rapidly expanded artificial intelligence applications for the diagnosis and classification of heart failure (HF). This review is designed to provide the clinician with the basics of ML, as well as this technologies future utility in HF diagnosis and the potential impact on patient outcomes. RECENT FINDINGS Recent studies applying ML methods to unique data sets available from electrocardiography, vectorcardiography, echocardiography, and electronic health records show significant promise for improving diagnosis, enhancing detection, and advancing treatment of HF. Innovations in both supervised and unsupervised methods have heightened the diagnostic accuracy of models developed to identify the presence of HF and further augmentation of model capabilities are likely utilizing ensembles of ML algorithms derived from different techniques. SUMMARY This article is an overview of recent applications of ML to achieve improved diagnosis of HF and the resultant implications for patient management.
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Affiliation(s)
- William E Sanders
- University of North Carolina at Chapel Hill, Chapel Hill
- CorVista Health, Inc., Cary, North Carolina, USA
| | - Tim Burton
- CorVista Health, Toronto, Ontario, Canada
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46
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Angelaki E, Marketou ME, Barmparis GD, Patrianakos A, Vardas PE, Parthenakis F, Tsironis GP. Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach. J Clin Hypertens (Greenwich) 2021; 23:935-945. [PMID: 33507615 PMCID: PMC8678829 DOI: 10.1111/jch.14200] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/07/2021] [Accepted: 01/10/2021] [Indexed: 01/19/2023]
Abstract
Cardiac remodeling is recognized as an important aspect of cardiovascular disease (CVD) progression. Machine learning (ML) techniques were applied to basic clinical parameters and electrocardiographic features, in order to detect abnormal left ventricular geometry (LVG) even before the onset of left ventricular hypertrophy (LVH), in a population without established CVD. The authors enrolled 528 patients with and without essential hypertension, but no other indications of CVD. All patients underwent a full echocardiographic evaluation and were classified into 3 groups; normal geometry (NG), concentric remodeling without LVH (CR), and LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). The authors trained supervised ML models to classify patients with abnormal LVG and calculated SHAP values to perform feature importance and interaction analysis. Hypertension, age, body mass index over the Sokolow‐Lyon voltage, QRS‐T angle, and QTc duration were some of the most important features. Our model was able to distinguish NG from CR+LVH combined, with 87% accuracy on an unseen test set, 75% specificity, 97% sensitivity, and area under the receiver operating curve (AUC/ROC) equal to 0.91. The authors also trained our model to classify NG and CR (NG + CR) against those with LVH, with 89% test set accuracy, 93% specificity, 67% sensitivity, and an AUC/ROC value of 0.89, for a 0.4 decision threshold. Our ML algorithm effectively detects abnormal LVG even at early stages. Innovative solutions are needed to improve risk stratification of patients without established CVD, and ML may enable progress in this direction.
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Affiliation(s)
- Eleni Angelaki
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece.,Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Maria E Marketou
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece
| | - Georgios D Barmparis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece
| | | | - Panos E Vardas
- Department of Cardiology, Heraklion University Hospital, Heraklion, Greece.,Heart Sector, Hygeia Hospitals Group, Athens, Greece
| | | | - Giorgos P Tsironis
- Institute of Theoretical and Computational Physics and Department of Physics, University of Crete, Heraklion, Greece.,Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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47
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Lin GM, Lu HHS. Electrocardiographic Machine Learning to Predict Left Ventricular Diastolic Dysfunction in Asian Young Male Adults. IEEE ACCESS 2021; 9:49047-49054. [DOI: 10.1109/access.2021.3069232] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
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48
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Kwon JM, Kim KH, Eisen HJ, Cho Y, Jeon KH, Lee SY, Park J, Oh BH. Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2020; 2:106-116. [PMID: 36711179 PMCID: PMC9707919 DOI: 10.1093/ehjdh/ztaa015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Revised: 11/10/2020] [Accepted: 11/20/2020] [Indexed: 02/01/2023]
Abstract
Aims Although heart failure with preserved ejection fraction (HFpEF) is a rapidly emerging global health problem, an adequate tool to screen it reliably and economically does not exist. We developed an interpretable deep learning model (DLM) using electrocardiography (ECG) and validated its performance. Methods and results This retrospective cohort study included two hospitals. 34 103 patients who underwent echocardiography and ECG within 1 week and indicated normal left ventricular systolic function were included in this study. A DLM based on an ensemble neural network was developed using 32 671 ECGs of 20 169 patients. The internal validation included 1979 ECGs of 1979 patients. Furthermore, we conducted an external validation with 11 955 ECGs of 11 955 patients from another hospital. The endpoint was to detect HFpEF. During the internal and external validation, the area under the receiver operating characteristic curves of a DLM using 12-lead ECG for detecting HFpEF were 0.866 (95% confidence interval 0.850-0.883) and 0.869 (0.860-0.877), respectively. In the 1412 individuals without HFpEF at initial echocardiography, patients whose DLM was defined as having a higher risk had a significantly higher chance of developing HFpEF than those in the low-risk group (33.6% vs. 8.4%, P < 0.001). Sensitivity map showed that the DLM focused on the QRS complex and T-wave. Conclusion The DLM demonstrated high performance for HFpEF detection using not only a 12-lead ECG but also 6- single-lead ECG. These results suggest that HFpEF can be screened using conventional ECG devices and diverse life-type ECG machines employing the DLM, thereby preventing disease progression.
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Affiliation(s)
- Joon-myoung Kwon
- Department of Critical Care and Emergency Medicine, Mediplex Sejong Hospital, Incheon, South Korea,Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea,Medical Research Team, Medical AI, Co. Seoul, South Korea,Medical R&D Center, Body Friend, Co. Seoul, South Korea
| | - Kyung-Hee Kim
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea,Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea,Corresponding author. Tel: 82-32-240-8245, Fax: 82-32-240-8094,
| | - Howard J Eisen
- Penn State Heart and Vascular Institute, Pennsylvania State University/Milton S. Hershey Medical Center, Hershey, PA, USA
| | - Younghoon Cho
- Medical R&D Center, Body Friend, Co. Seoul, South Korea
| | - Ki-Hyun Jeon
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea,Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Soo Youn Lee
- Artificial Intelligence and Big Data Research Center, Sejong Medical Research Institute, Bucheon, South Korea,Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Jinsik Park
- Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
| | - Byung-Hee Oh
- Division of Cardiology, Cardiovascular Center, Mediplex Sejong Hospital, Incheon, South Korea
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Farjo PD, Yanamala N, Kagiyama N, Patel HB, Casaclang-Verzosa G, Nezarat N, Budoff MJ, Sengupta PP. Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study. ACTA ACUST UNITED AC 2020; 1:51-61. [PMID: 37056293 PMCID: PMC10087019 DOI: 10.1093/ehjdh/ztaa008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 10/06/2020] [Accepted: 10/13/2020] [Indexed: 02/07/2023]
Abstract
Abstract
Aims
Coronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.
Methods and results
In this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years.
Conclusion
ML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease.
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Affiliation(s)
- Peter D Farjo
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Naveena Yanamala
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
- Institute for Software Research, School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA
| | - Nobuyuki Kagiyama
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
- Department of Digital Health and Telemedicine R&D, Juntendo University, 211 Hongo, Bunkyo City, Tokyo 113-8421, Japan
- Department of Cardiovascular Biology and Medicine, Juntendo University, 211 Hongo, Bunkyo City, Tokyo 113-8421, Japan
| | - Heenaben B Patel
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Grace Casaclang-Verzosa
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
| | - Negin Nezarat
- Department of Medicine, Lundquist Institute, Harbor-UCLA Medical Center, 1124 West Carson St, Torrance, CA 90502, USA
| | - Matthew J Budoff
- Department of Medicine, Lundquist Institute, Harbor-UCLA Medical Center, 1124 West Carson St, Torrance, CA 90502, USA
| | - Partho P Sengupta
- Division of Cardiology, Department of Medicine, West Virginia University Heart and Vascular Institute, 1 Medical Center Drive, Morgantown, WV 26506, USA
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Digital Phenotyping of Myocardial Dysfunction With 12-Lead ECG: Tiptoeing Into the Future With Machine Learning. J Am Coll Cardiol 2020; 76:942-944. [PMID: 32819468 DOI: 10.1016/j.jacc.2020.07.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 07/01/2020] [Indexed: 02/06/2023]
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